Existing methods for building extraction from remotely sensed images strongly rely on aerial or satellite-based images with very high resolution, which are usually limited by spatiotemporally accessibility and cost. In contrast, relatively low-resolution images have better spatial and temporal availability but cannot directly contribute to fine- and/or high-resolution building extraction. In this paper, based on image super-resolution and segmentation techniques, we propose a two-stage framework (SRBuildingSeg) for achieving super-resolution (SR) building extraction using relatively low-resolution remotely sensed images. SRBuildingSeg can fully utilize inherent information from the given low-resolution images to achieve high-resolution building extraction. In contrast to the existing building extraction methods, we first utilize an internal pairs generation module (IPG) to obtain SR training datasets from the given low-resolution images and an edge-aware super-resolution module (EASR) to improve the perceptional features, following the dual-encoder building segmentation module (DES). Both qualitative and quantitative experimental results demonstrate that our proposed approach is capable of achieving high-resolution (e.g. 0.5 m) building extraction results at 2×, 4× and 8× SR. Our approach outperforms 8 other methods with respect to the extraction result of mean Intersection over Union (mIoU) values by a ratio of 9.38%, 8.20% and 7.89% with SR ratio factors of 2, 4, and 8, respectively. The results indicate that the edges and borders reconstructed in super-resolved images serve a pivotal role in subsequent building extraction and reveal the potential of the proposed approach to achieve super-resolution building extraction. Our code is available at https://github.com/xian1234/SRBuildSeg.

In this research paper, change detection based methods were considered to find collapsed and intact buildings using radar remote sensing data or radar imageries. Main task of this research paper is collection of most relevant scientific research in field of building damage assessment using radar remote sensing data. Several methods are selected and presented as best methods in present time, there are methods with using interferometric coherence, backscattering coefficients in different spatial resolution. In conclusion, methods are given in end, which show, which methods and radar remote sensing data give more accuracy and more available for building damage assessment. Low resolution Sentinel-1A/B radar remote sensing data are recomended as free available for monitoring of destruction degree in microdistrict level. Change detection and texture based method are used together to increase overall accuracy. Homogeneity and Dissimilarity GLCM texture parameters found as better for separation of a collapsed and intact buildings. Dual polarization (VV,VH) backscattering coefficients and coherence coefficients (before earthquake and coseismic) were fully utilized for this study. There were defined the better multi variable for supervised classification of none building, damaged and intact buildings features in urban areas. In this work, we were achieved overall accuracy 0.77, producer’s accuracy for none building is 0.84, for damaged building case 0.85, for intact building 0.64. Amatrice town was chosen as most damaged from 2016 Central Italy Earthquake.

One of the important research directions in the field of photovoltaics is integration with construction. The integration of solar cell systems with a building can reduce installation costs and help optimize the used space. One of the interesting types of cells is dye-sensitized solar cells. In addition to their interesting properties, they also have aesthetic value. In the classic arrangement, they are constructed using glass with a transparent conductive layer (TCL). This article describes replacing a classic glass counter electrode with an electrode based on a ceramic tile and nickel foil. This solution makes it possible to expand their construction applications. The advantage of this solution is full integration with construction while simultaneously generating electricity. A dye-sensitized solar cell was built layer-by-layer on ceramic tile and nickel foil. An atomization method was used to deposit fluorine-doped tin oxide, and then a screen printing method was used to deposit a platinum layer. The electrical parameters of the manufactured DSSCs with and without a counter electrode tile were characterized by measuring their current-voltage characteristics under standard AM 1.5 radiation. A dye-sensitized solar cell integrated with ceramic tiles and nickel foil was produced and displayed an efficiency of over 4%.

Cost estimating based on building cost index plays an important role in project planning and management by providing accurate cost information. Recently, tremendous advances in cost estimating has been made but serious inaccuracies in it are still too frequently witnessed. This study aims to improve estimating accuracy for residential building costs in New Zealand. In this study, the New Zealand house prices index is involved in the transfer function models to produce forecasts of building costs for one-storey house, two-storey house, and town house in New Zealand. To demonstrate the effectiveness of the proposed models, this study compares the estimate results of the transfer function models with the univariate ARIMA models. The results indicate that the proposed transfer function models can achieve better outcomes than ARIMA models by considering the causality between building costs and New Zealand house prices. During the modelling process, the better cost estimation approach can be identified, and the movements of building costs are shown.

In a new construction of a seventeen-storey building, it is necessary to remove a prop on the second floor, which has made it possible to complete the structure of the building. In the drafting of the executive project, no security measures were described, because it was understood that they were not necessary. In the construction phase, the need to monitor the process was raised, and to be able to stop it, or reverse it if necessary. It is a reinforced concrete structure, with metal elements and composite elements, with a torsional behavior of the cores making the slabs serve as horizontal diaphragms, so the final behavior could be different from what was expected. To make this process possible, two pairs of four metallic cantilevers were introduced into which four Hydraulic pistons were inserted. Before starting the process, they were loaded to be sure that they would not fail. Strain gauges were also placed to control the stresses and how the situation changed during the unshoring process. The process of cutting the provisional pillar was a success, obtaining less deformations than expected, having a complementary safety.

Building roofs are sources of unwanted heat for buildings situated in zones with a warm climate. Thus, reflective coatings have emerged as an alternative to reject a significant fraction of solar energy received by roofs. In this research, the thermal behavior of concrete slab-type roofs with traditional and solar reflective coatings was simulated using a computational tool. Weather data from four cities in Mexico with a warm climate were used as boundary conditions. This tool is an in-house code based on the Finite Volume Method developed by the author to perform building components simulations. The code was validated with experimental data from previous work. A series of comparative simulations were developed, taking a gray roof as a control case. The results showed that for the roof without thermal insulation (single roof), the solar reflective coatings reduced the exterior surface between 11 and 16∘C. Consequently, the single roofs’ daily heat gain was reduced by a factor ranging between 41 and 54%. On the other hand, for the insulated roof, the reflective coatings reduced the exterior surface temperature between 17 and 21∘C. At the same time, the daily heat gain of composite roofs was reduced between 37 and 56%.

This article addresses the question why initiatives in the field of green business and sustainable development often fail. Therefore, it dismantles some typical patterns of failure and shows – as a case study – how these patterns can be challenged through an innovative educational concept: the green business and sustainable development school. The applied methodology is a real-life project that is designed through blended, interdisciplinary elements from business model canvas, Theory U, participation and design thinking. The results of the school initiative are discussed and evaluated by four distinctive stakeholder groups and outline the school’s supporting potential to overcome typical patterns of failure by the younger generation in the future. This article concludes with ideas to enhance the school concept reaching out to even more stakeholder-groups to increase its reliability and viability.

This study explores the perspectives and practices of architects regarding the reuse of wood in construction across five South-Central European countries: Slovenia, Serbia, North Macedonia, Bosnia and Herzegovina (BiH), and Montenegro. Based on a survey of architectural professionals, the research explores their attitudes, challenges, and motivations for using reclaimed wood, with a focus on circular construction principles. Key findings reveal unanimous agreement among re-spondents that both their profession and government entities inadequately support or promote wood reuse within the context of circular construction. While architects value reclaimed wood, there are country-specific variations. Environmental benefits, unique aesthetics, historical value, and sustainability are cited as primary motivators for reclaimed wood use. Additionally, re-spondents emphasize the ecological aspect as the most important consideration in wood product reuse, followed by economic and technological factors. Common concerns revolve around the long-term quality and maintenance of wood products. This research provides insights into the challenges and opportunities surrounding reclaimed wood use in South-Central Europe, high-lighting the need for support mechanisms and increased awareness to advance circular construc-tion practices in the region. These findings can have implications for architects and investors by identifying market opportunities, promoting eco-friendly practices, and providing valuable in-sights for future building designs with a focus on reclaimed wood elements within circular con-struction.

With the increasing adoption of building integrated photovoltaics (BIPV), concerns arise about potential glare. While recommended criteria to assess glare exist, it is challenging to apply these in the spatial and temporal domains and communicate the complex data to planning authorities and clients. This paper presents a new computational workflow using annual daylight simulation, material modelling using bi-directional scattering distribution functions (BSDFs) and image-based postprocessing to obtain 3-dimensional renderings of cumulative annual irradiance and glare duration on the built environment. The annual daylight simulation considers relevant sun positions in high temporal resolution (15-minute timesteps) and measured BSDFs to model different PV materials. The postprocessing includes a relative irradiance visualisation comparing the impact of a proposed PV proportional to a reference material. It also includes a new spatio-temporal workflow to assess the glare duration based on recommended thresholds. This workflow is demonstrated with a case study of a proposed PV roof for a church, assessing the glare potential of two different PV materials. The visualisations indicate glare durations well below the thresholds with satinated PVs, and in noncritical zones outside observer positions with standard PVs. Thus the proposed PV roof does not cause any disturbing glare.

The increased focus on sustainability in response to climate changes has given rise to many new initiatives to meet the rise in building load demand. The concept of distributed energy resources (DER) and optimal control of supply to meet power demands in buildings have resulted in growing interest to adopt microgrids for a precinct or a university campus. In this paper, a model for an actual physical microgrid has been constructed in OPAL-RT for real-time simulation studies. The load demands for SIT@NYP campus and its weather data are collected to serve as input to run on the digital twin model of DERs of the microgrid. The dynamic response of the microgrid model in response to fluctuations in power generation due to intermittent solar PV generation and load demands are examined via real-time simulation studies and compared with the response of the physical assets. It is observed that the simulation results match closely to the performance of the actual physical asset. As such, the developed microgrid model offers plug-and-play capability which will allow power providers to better plan for on-site deployment of renewable energy sources and energy storage to match the expected building energy demand.

European cities are motivated to act towards the achievement of climate-neutral mobility solutions. Often, though, they are facing many challenges when bringing (innovative) sustainable mobility solutions forward. Capacity building that fills skills gap and/or enables acquisition of new ones related to the planning and implementation of such solutions can empower local/ regional authorities to identify them, adopt them and eventually deliver them properly. The aim of this paper is to present the Key Performance Indicator (KPI) framework that has been used for the assessment of the effectiveness of the Learning and Exchange Programme applied in an EU-funded project. It presents the methodological steps for the adoption of the KPIs, as well as the tools used for the selection of the KPI data and the KPI monitoring at project level. It also presents the results from the application of the framework for assessing the knowledge performance towards the deployment of sustainable mobility solutions. It finally reflects on recommendations for applying the KPI framework to other cases and thematic contents.

Building situated in the flow path of wind is subjected to differential velocity and pressure distribution around the building envelope. Wind effects are influenced and vary for each individual shape of the tall building. Tall building structures are considered as cantilever structures with fixed ends at the ground. Wind exponential velocity acting along the height of the building makes velocity and pressure distribution more complex as the height of the building structure increases. This study discussed the effect of wind on an irregular cross-section shape. The study was conducted numerically with a building model placed in a virtual wind tunnel using the ANSYS (CFX) software tool. Wind effects are investigated on building model situated in a terrain category - II defined in IS: 875 (Part 3): 2015 wind scale model of 1:100 and turbulence intensity is 5 % and power law index α is considered as 0.143. Validation and verification of the study were made by comparing pressure coefficients on faces of a rectangular model of similar floor area and height as taken for a C-plan diamond-shaped model under similar boundary conditions, wind environment, and solver setting of numerical setup. The values of surface pressures generated on the recessed faces of the model and wind flow patterns within the recessed cavity were studied at wind incident angles 0°, 300, 60°, 1050, 1350 & 1800. The critical suction on all the recessed faces was observed to be at a 105° angle of wind attack.

The Agile Building Adaptation (AgiBuild) framework is the adoption and adaptation of the large-scale agile framework for building adaptation projects. The agile methodology is proven to drive innovation by focusing on adaptation to change and user centricity. Similarly, the authors envision that the AgiBuild framework can fundamentally change the way that buildings are re-designed, refurbished, and operated. The AgiBuild framework is developed from the need of the building adaptation industry to manage uncertainties, overcome communication barriers, and improve innovation. In this study, a literature review of Agile and its impact on building adaptation projects is undertaken. Based on this systematic literature review, this paper defines the AgiBuilt framework and provides its benefits and barriers to implementation. A key finding of the literature review is that leadership influence, and adequate training form the key foundation for the implementation of the AgiBuild Framework. In defining the AgiBuild framework, the paper describes its components and how its implementation is likely to proceed. The authors propose that by adopting the AgiBuild framework, the industry can transform itself into a highly innovative and user-centred industry to improve productivity and performance of the construction industry..

The paper is based on the scientific outcome of a PhD Thesis. It introduces the generic, model-based, reusable, and extensible conceptual framework to incorporate Facility Management data based within the three-dimensional model-based design and construction of an asset to enable smart applications, which are introduced. The conceptual framework is composed of empirical data from expert interviews, questionnaires, and factual analysis from 13 projects of varied sizes of public and private clients. It shows which phases need which data, who needs them, and which added value can be generated if intelligent data structuring is used at the beginning of the construction project and bridges the gap between requirement and practice. The term “smart application” is introduced.

This paper reports aligned activities to promote energy and resource-efficient construction practice in Vietnam. First, the governance framework is introduced, including government decrees and technical standards. Then, a laboratory with building physics measurement technology is designed and partly set up at the local partner Vietnam Institute for Building Materials (VIBM), which can be used to determine the essential characteristic values required for the implementation of energy standards. The basis for the prioritization of the characteristic values are the requirements of the National technical regulation on energy efficient buildings of Vietnam – QCVN09:2017/BXD. Furthermore, basic characteristic values from international standards are described, which can also be used for calculations to optimize the energy consumption of buildings. In order to be able to carry out transient hygrothermal computer simulations, special characteristic values were also included. These are particularly useful for the research and development of new building materials and the evaluation of entire buildings in terms of thermal and moisture protection In order to communicate the material properties to the market and enable informed decision making by the stakeholders, a labeling system has been developed. Thereby, the practical means for implementing governance instruments are provided, and the related technical applications are supported.

Building envelope can play a crucial role in building improvement efficiency and the adoption of Phase Change Materials (PCMs) coupled with transparent elements may (i) allow a better control of the heat flows from/to the outdoor environment, (ii) increase the exploitation of solar energy at building scale and (iii) modulate the light transmission to avoid glare effects. Starting from a literature review of experimental works, this research identifies the main possible integration of PCM in transparent/translucent building envelope components (in glazing, in shutters and in multilayer façade system) drawing a global picture potentials and limitations of these technologies. Transparent envelopes with PCMs have been classified from the simplest “zero” technology, which integrates the PCM into the double glass unit (DGU), to more complex solutions – with different numbers of cavities of the glass (TGU), different position of PCM layer (internal/external shutter), and in combination with other materials (TIM, aerogel, prismatic solar reflector, PCM curtain controlled by an electric pump). The results of the analysis are resumed in a SWOT (strengths, weakness, opportunities and threats) analysis table to underline strengths and weaknesses of transparent building envelope components with PCMs, and to indicate opportunities and threats for future research and building application.

This paper presents a novel approach for semantic segmentation of building roof in dense urban environment with Deep Convolution Neural Network (DCNN) using imagery acquired by a Chinese Very High Resolution (VHR) satellite mission, i.e. GaoFen-2 (GF-2). To provide an operational end-to-end work flow for accurate build roof mapping with feature extraction as well as image segmentation, a fully convolutional DCNN with both convolutional and deconvolutional layers is designed to perform the VHR image analysis for labeling pixels. Since the diverse urban patterns and building styles in large areas, sample image data sets of building roof and non-building roof are collected over different metropolitan regions in China. We selected typical cities with dense urban environment in each metropolitan region as study areas for collecting training and test samples. High performance cluster with GPU-mounted workstations is employed to perform the model training and optimization. With the building roof samples collected over different cities, the predictive model with multiple NN layers is developed for building roof labeling. The validation of the building roof map shows that the overall accuracy(OA) and the mean Intersection Over Union( mIOU) of DCNN based segmentation are 94.67%, 0.85 respectively, while CRF-refined segmentation achieved OA of 94.69% and mIOU of 0.83. The results suggest that the proposed approach is a promising solution for building roof mapping with VHR images over large areas across different urban and building patterns. With the operational acquisition of GF2 VHR imagery, it is expected to develop an automated pipeline for operational built-up area monitoring and timely update of building roof map over large areas.

The benefits of cycling as a sustainable transportation mode are widely recognized. Cycling to work offers numerous advantages for individuals, employers, the environment, and society, ranging from improved physical health, wellbeing, and productivity to enhanced public health and reduced carbon emissions. Many governments around the world have implemented various policy measures, interventions, and initiatives to promote cycling. Some cities and towns have encouraged workplaces to provide bike-friendly amenities, such as secure bike parking/storage and shower and changing facility (referred to as end-of-trip facilities), to accommodate cyclists and motivate more employees to embrace sustainable transportation options. However, the presence or absence of such amenities has been found to both positively influence cycling behavior or deter some individuals from cycling to work. Despite the desirability of these amenities, the number of office buildings equipped with bike storage and shower/changing facility remains small, and their economic value is not well understood. In this paper, we aim to investigate whether the presence of cycling-supportive facilities could add value to office buildings. Specifically, we conduct a study using data from office buildings across England as of the end of 2021. We employ hedonic techniques to examine whether office buildings with bike storage and/or shower facility command a rent premium and whether the existence of a rent premium for bike storage and/or shower facility vary by location and across regions in England, independently from any premium associated with BREEM certification. We find that the buildings with bike storage and shower facility secure rent premiums respectively. The level of rent premiums varies with location and regions. The variations of premium are driven by the shortage of the supply of these facilities.

A green, fast and selective approach for the synthesis of mono-substituted closo-decaborate derivatives [2-B10H9COR]n– has been established via a nucleophilic addition reaction between the carbonyl derivative of closo-decaborate [2-B10H9CO]– and the corresponding Grignard reagent RMgX, where R is the ethyl, iso-propyl, pentyl, allyl, vinyl, and propynyl groups. This approach is accomplished under mild conditions with 70–80% yields. The significance of these derivative is their ability to constitute building blocks for polymeric integration via the allyl, vinyl and propynyl substituents. All products were characterized by 11B, 1H, and 13C NMR, elemental analysis and mass spectrometry.

Smart buildings concept aims at the use of the smart technology to reduce energy consumption as well as improvement of the comfort conditions and users’ satisfaction. It is based on the use of smart sensors to follow both outdoor and indoor conditions as well as software for the control of comfort and security devices. The optimal control of the energy devices requires software for indoor temperature forecasting. This paper presents an ANN – based model for the indoor temperature forecasting. The model is developed using data recoded in an old building of the engineering school Polytech’Lille. Data covered both indoor and outdoor conditions. Analysis of the relevance of the input parameters allowed to develop a simplified forecasting model of the indoor temperature that uses only the outdoor temperature as well as the history of the façade temperature as input parameters. The paper presents successively, data collection, the ANN concept used in the temperature forecasting, and finally the ANN model developed for the façade and indoor forecasting. It shows that an ANN-based model using outdoor and façade temperature sensors provides a good forecasting of the indoor temperature. This model could be used for the optimal control of buildings energy devices.

In the absence of a treaty protocol or verification regime, the Biological and Toxin Weapons Convention (BWC) instituted Confidence-Building Measures (CBMs) as a mechanism to increase confidence in compliance, by enhancing transparency and mitigating ambiguities regarding states parties’ biological activities. While a promising tool to support treaty compliance, low participation, concerns regarding the completeness and accuracy of CBM submissions, a dearth of analysis, and restricted access to many submissions have limited CBMs’ value. Through interviews with 53 international experts—38 from BWC delegations and 15 independent experts—we identified concrete opportunities to increase CBMs’ value while mitigating the burden on states parties. This study supports states parties’ efforts in the BWC Working Group on the Strengthening of the Convention, as part of a series of research on BWC assurance that aims to characterize challenges around BWC verification and increase certainty in BWC compliance.

The wind flow patterns and pressure distribution around a plus-shaped building are significantly influenced by the dimensions of its re-entrant corners and the overall building height. The present study aims to find the wind effects and study of the pressure distribution with different flow patterns. Plan area of the building is around 300 square meter while the height of the building is 50 meters by keeping the equal length of the re-entrant corners. The research is performed using ANSYS CFX academic version and the results are depicted in various forms such as pressure coefficient (Cpe), stream lines in both vertical as well as in specific surface and pressure contours. The analysis is carried out by varying the wind incident angles, from 0° to 90° at the interval of 15°. The building dimension are scale down to perform the test on length scale of 1:100. Since the considered building model exhibits symmetry along both the axes, present study mostly emphasis on examining re-entrant corners located in the building. This method yields valuable information about how air circulates and how pressure is distributed across a broad spectrum of wind angles. This study provides valuable insights into flow circulation and pressure distribution for a wide range of wind angles.

Buildings can play an important role in reducing GHG emissions through increased energy efficiency. The European Commission issued a proposal in 2021 suggesting that all new buildings should be “zero-emission Buildings” (ZEB), aiming at a zero GHG emission building stock by 2050.The extent to which ZEB can contribute to reduced GHG emissions, however, varies between countries, due to different energy systems. It is also important to consider other environmental effects to avoid that climate benefits come with unintended consequences.Here, we explore the life-cycle environmental performance for a ZEB in a case where electricity and heating is largely fossil-free. The assessment concentrates on i) environmental impact of the use stage in relation to the product stage, ii) the interrelation between different energy sources, with attention on household electricity, and iii) the performance for more impact categories than primary energy use and climate change. While our results generally support the use of ZEBs from an environmental perspective, they also show that the climate benefit in this setting is marginal. However, given that energy systems are connected and energy savings in one place can reduce the demand for fossil energy elsewhere, the climate benefit of ZEBs is likely underestimated. Besides methdological implications for future studies, this indicates that current EU policy is promising, as incentives for implementation of ZEBs is unaffected by domestic effects.

Shared public buildings have become centers of innovation, integrating advanced technologies to meet evolving societal needs. With a heightened emphasis on occupants’ health and well-being, these buildings serve as hubs for technological convergence, facilitating seamless connectivity and intelligent data analysis and management. Within this context, environmental monitoring emerges as a foundational element, pivotal to all aspects of building management. This article provides findings from the nationally funded RE-START project, which focuses on shared public buildings, with special regard to educational and medical facilities. The project explores enhanced indoor air quality monitoring, focusing in particular on CO2 concentration that is directly correlated with occupancy, as a fundamental element for developing health and safety protocols, energy efficiency strategies, integration of smart building technologies, and data-driven energy management. The intersection of environmental monitoring, energy efficiency, security, and IoT technologies in in indoor spaces are relevant. The outcomes of the study reveal the delicate nature of all the involved components, that need to be carefully developed in an integrated manner.

Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive. Traditional methods for this type of work commonly comprise of engaging building surveyors to undertake a condition assessment which involves a lengthy site inspection to produce a systematic recording of the physical condition of the building elements, including cost estimates of immediate and projected long-term costs of renewal, repair and maintenance of the building. Current asset condition assessment procedures are extensively time consuming, laborious, and expensive and pose health and safety threats to surveyors, particularly at height and roof levels which are difficult to access. We propose a method for automated detection and localisation of key building defects from images using deep learning and convolution neural networks. The proposed model is based on a pre-trained VGG-16 classifier with Class Activation Mapping (CAM) for object localisation. The model has proven to be robust and able to accurately detect and localise mould growth, stains, and paint deterioration defects arising from dampness in buildings. The approach is being developed with potentials to scale-up to support automated detection of defects and deterioration of buildings in real-time using mobile devices and drones.

High-rise buildings represent technological, urban and life-style trends of the modern urban landscape, yet there are limited data regarding their embodied carbon and environmental impacts, particularly when compared to low- or mid-rise buildings. Given that the projected growth of the global urban population by 2050 requires cities with higher density and potentially greater number of high-rise buildings, it is crucial to develop a clear understanding of the embodied carbon and environmental impacts of high-rise buildings. This article utilizes the building in-formation modelling (BIM) based life cycle assessment (LCA) in Revit and Tally to examine the embodied carbon and environmental impacts of an actual high-rise building structure case-study in Chicago that uses hybrid concrete steel structure. The results show that the embodied carbon and environmental impacts of the high-rise building structure are dominated by the impacts of the product stage in the building life cycle, and concrete as the main structural material. Specifically, this study reveals that concrete constitutes a substantial 91% share of the total mass of the building structure, with 74% contribution to life cycle global warming potential, 53% to acidification po-tential, 74% to eutrophication potential, 74% to smog formation potential, and 68% to non-renewable energy. On the other hand, steel accounts for 9% of the building structure mass, estimated to constitute 26% of the global warming potential, 47% to acidification potential, 26% to eutrophication potential, 26% to smog formation potential, and 32% to non-renewable energy.

Vernacular architecture constitutes a rich source of information and ancestral knowledge that can become a key resource for sustainable development through its passive design strategies, which effectively respond to local climatic and weather conditions using locally sourced materials for the construction of its supporting structure and enveloping elements, as well as spatial organization and the incorporation of a buffer area (patio) that optimize the use of renewable resources. This qualitative study analyzes a traditional housing typology with a central patio located in the Historic Center of Azogues city, Ecuador, to evaluate its interior thermal comfort through in-situ monitoring in the different building spaces. Using the open-source software Open Studio and EnergyPlus, a simulation model was built to assess the annual thermal performance of the house. Field records were used to verify the effectiveness of the strategies that respond to the climatic conditions of the area. The analysis carried out on the passive strategies used in the selected house with regard to natural ventilation, solar protection, and thermal insulation, which depend on various aspects of the building, such as its location, internal space arrangement, design of openings (doors and windows), among others. The thermal simulations reveal that the traditional house located in the Historic Center of Azogues city is well adapted to the local climate, although interior thermal comfort is not entirely satisfactory.

There is a substantial lack of data relating to the hygrothermal traits of existing buildings and building materials. Moisture analysis in building envelopes is 1D (one-dimensional), and 2D and 3D effects and feedback can be neglected. Many European standards treat a moisture percentage in building envelopes as surface and interstitial moisture condensation, such as BS EN ISO 10211, BS EN ISO 13788, BS EN ISO 15148, and BS EN 15026. In this manuscript, there was a tendency to capture a relative quantity of superficial moisture, by metering spots on the inner and outer surface of wall lining systems with a moisture meter. The metering specimens were the buildings with different envelopes: a wooden hut-house, a masonry building and a building partially sheeted in stone. The referent heights on wall linings, for taking the moisture data were specified as 50 cm for a wooden and masonry building and 80 cm for a stone-sheeted building, from the ground. The obtained numeric results were palpable with no regard to the current state of the weather outside. The listed conclusions highlight the significance of a well-tailored building envelope, and its dependence on weather, junctions, gaskets, appropriate materials, etc.

In Europe, the recent application of regulations oriented to zero-energy buildings and climate neutrality in 2050 has led to a reduction in energy consumption for heating and cooling in the construction sector. The thermal insulation of the building envelope plays a key role in this process and the requirements about the maximum allowable thermal transmittance are defined by country-specific guidelines. Typically, high insulation values provide low energy consumption for heating, however, they may paradoxically imply the risk of overheating in summer period and thus negatively affect the overall performance of the building. In addition, the embodied energy and related emissions caused by the manufacturing and transportation processes of thermal insulation cannot be further neglected in the evaluation of the best optimal solution. Therefore, this paper aims to evaluate the influence in terms of embodied and operational energy of various walls’ thermal insulation thicknesses on residential buildings in Europe. To this end, the EnergyPlus engine was used for the energy simulation within Ladybug & Honeybee tools, by parametrically conducting multiple iterations; 53 variations of external wall U-value, considering high and low thermal mass scenarios, were simulated for 100 reference cities of the European context, using a representative multifamily building as a reference. The results demonstrate that massive walls generally perform better than lightweight structures and, of course, the best solution in terms of energy varies according to each climate. The optimal values are graphically reported on the map of Europe according to specific climatic features, providing a guidance for new constructions and building retrofit.

The construction field uses up over one-third of the global energy consumption and contribute to 40% of CO2 emissions according to the International Energy Agency (IEA) and the 2020 annual reporter of United Nation, Goal 11 (Make cities inclusive, safe, resilient and sustainable) which discusses sustainable, safe and efficient buildings. Therefore, Morocco has a commitment to this program by publishing the law 47-09 of energy efficiency. This work aims to study the energy efficiency of two types of building, a conventional and a natural building. Conventional building is constructed using concrete, while the natural one uses sand clay and straws. As for the technique of making the natural building, it perpetually follows the same approach accustomed in rural zones of Atlas Mountains in Morocco. In this research we also simulate, temperature and humidity variation inside these buildings using TRNSYS software. Sketch Up software was employed to design these houses. The weather database is used for a typical meteorological year (TMY). In the case of natural building, many building configurations were simulated: roof insulation, floor insulation, different types of glazing and sun protection. What's more, the thermal comfort is revealed to be more conspicuous in the case of natural building.

This study explores the integration of Building Information Modelling (BIM) and Virtual Tours in the Architecture Engineering and Construction (AEC) industry, focusing on Facility Management (FM) and Computerized Maintenance Management Systems (CMMS). The research addresses the need for different levels of detail in BIMs across various building phases and the potential of Virtual Tours as a complement to BIMs in IoT-based FM. The study’s methodology includes a literature review, a pilot-demonstrator case study, installation of monitoring and IoT control devices, execution of BIMs and Virtual Tour models, and development of links between system elements. The case study is based on the Controlled Environments for Living Lab Studies (CELLS), a unique facility designed for flexible monitoring and remote-control processes. The findings of this research could provide valuable insights into the potential of Virtual Tours to complement and connect to BIMs within a life cycle perspective, enhancing the usability of digital twins for nontrained users and contributing to the evolution of the AEC industry.

The authors of the article analyze the existing methods and models of technological preparation of machine-building industries. The structure of a three-level simulation model with network-centric control, the structures of individual elements of the simulation model and the process of simulation modeling are described. The criteria for choosing a rational option for the processing technological route have been determined. During the research, a simulation program was implemented in C++. It allows you to select the optimal scenario for the operation of a production site based on two criteria: time and cost. The volume of implementation is about 2x103 lines of code. A diagram of the modeling algorithm for the implemented program, a description of the classes and their interactions are given in the article. The developed simulation model was tested at a machine-building enterprise using the example of the “Pusher” part, manufactured under single-unit production condi-tions. The technological equipment used for the manufacture of this part was formed in the form of input data of the simulation model. The results of simulation modeling for the selected part are described. For each variant of the technological processing route, the values of variable costs and the duration of the production cycle were determined.

Recent technological improvements have made abundant changes in construction industry. In specific, some technical applications, such as Building Information Modeling (BIM) opens up many possibilities. Some studies have articulated the use of BIM and its advantages in construction, but most of them are theoretical, not practical. This study is to provide an insight to such obstacles in BIM research. By investigating a real project that could utilize BIM in planning and construction phases, the authors try to investigate a possible outline of advantages in BIM implementation. The study area was set to a railway construction site in South Korea. The site covers a multiple railway tracks, stations, telecommunication facilities, infrastructure facilities, railway structures, and so numerous. In the site, the authors have identified 12 errors in 7 projects that could be prevented if BIM was utilized before the construction. The total upfront costs required to provide a BIM for 7 projects were $116,348. On the other hand, the total costs required to fix the errors in 7 projects were $166,486. This can be regarded as the benefits of using BIM because if BIM was implemented then the associated errors are easily replaced. Therefore, the benefit-cost ratio can be estimated as 1.32 for one-month delay and 1.36 for a three-month delay.

The architecture, Engineering, and Construction (AEC) industry is dealing with serious challenges on a global scale, i.e., project delays, budget overruns, and insufficient stakeholder collaboration. In this context, Value Engineering (VE) and Building Information Modeling (BIM) have been used recently to address these challenges and cope with large-scale and complex construction projects. An extensive body of literature highlights significant advantages of integrated VE within BIM technology. Despite this, it is full practical potential has not yet been realized. For this reason, this paper aims to investigate the integration of Value Engineering (VE) and Building Information Modeling (BIM) within a Common Data Environment (CDE) for the Architecture, Engineering, and Construction (AEC) industry. In doing this research, a comprehensive framework was developed and systematically implemented into practice through the use of four interconnected modules: (M. 1); Creating CDE, (M. 2); Design BIM Model Development, (M. 3); Value Analysis / Engineering Model, and (M. 4); Value Engineering Study. Introducing a new term the ""VEIDEA"" databank based on the OmniClass classification system provides a structured database utilized to store, manage, and accumulate VE ideas. Moreover, the study incorporates the Analytical Hierarchy Process (AHP) to automate the evaluation phase. Such a framework seeks to assist designers and VE team members make informed decisions when choosing appropriate design alternatives. Lastly, a case study method was adopted to examine the proposed framework for an office building. Quantitative data showed a significant link between cost savings and VR integration with BIM technology. For instance, cost of the RC slabs dropped by 20%, while flooring materials cost dropped by a 39%. Empirically, evidence from the case study supports literature regarding t the potential for significant enhancements in both cost-effectiveness and overall project performance through this innovative integration. In the context of knowledge contribution, this study provides an innovative way to assist designers and VE team members in making well-informed decisions when choosing appropriate design alternatives.

At present, indoor localization is one of the core technologies of location-based services (LBS), and there exist numerous scenario-oriented application solutions. Visual features, as the main semantic information to help people understand the environment and thus occupy the dominant part, many techniques about indoor scene recognition are widely adopted. However, the engineering application problem of cell phone indoor scene recognition and localization has not been well solved due to insufficient semantic constraint information of building map and the immaturity of building map location anchors (MLA) matching positioning technology. To address the above problems, this paper proposes a cell phone indoor scene recognition and localization method with building map semantic constraints. Firstly, we build a library of geocoded entities for building map location anchors (MLA), which can provide users with "immersive" real-world building maps on the one hand and semantic anchor point constraints for cell phone positioning on the other. Secondly, using the improved YOLOv5s deep learning model carried on the mobile terminal, we recognize the universal map location anchors (MLA) elements in building scenes by cell phone camera video in real-time. Lastly, the spatial location of the scene elements obtained from the cell phone video recognition is matched with the building MLA to achieve real-time positioning and navigation. The experimental results show that the model recognition accuracy of this method is above 97.2%, and the maximum localization error is within the range of 0.775 m, and minimized to 0.5 m after applying the BIMPN road network walking node constraint, which can effectively achieve high positioning accuracy in the building scenes with rich MLA element information. In addition, the building map location anchors (MLA) has universal characteristics, and the positioning algorithm based on scene element recognition is compatible with the extension of indoor map data types, so this method has good prospects for engineering applications.

Currently there are strong and sustained growth trends observed in multi-disciplinary industrial technologies such as building-integrated photovoltaics and agrivoltaics, where renewable energy production is featured in building envelopes of varying degrees of transparency. Novel glass products can provide a combination of thermal energy savings and solar energy harvesting, enabled by either patterned-semiconductor thin-film energy converters on glass substrates, or by using luminescent concentrator-type approaches to achieve high transparency. Significant progress has been demonstrated recently in building integrated solar windows featuring visible light transmission of up to 70%, with electric power outputs of up to Pmax ~ 30-33 Wp/m2. Several slightly different designs were tested during 2021-2023 in a greenhouse installation at Murdoch University in Perth, Western Australia. The objective of this paper is to report on the field performance of these PV windows in the context of agrivoltaics, and to provide some detail of the performance differences measured in several solar window designs related to their glazing structure materials. Methods for the identification and quantification of long-term field performance differences and energy generation trends in solar windows of marginally different design types are reported. The paper also aims to outline the practical application potential of these transparent construction materials in built environments, focussing on the measured renewable energy figures and seasonal trends observed during the long-term study.

The building sector has a big potential to reduce the material resource demand needed for building construction and therefore, greenhouse gas (GHG) emissions. Digitalisation can help to make use of this potential and improve sustainability throughout the entire building’s life cycle. One way to address this potential is through the integration of Life-Cycle Assessment (LCA) into the building process by employing Building Information Modelling (BIM). BIM can reduce the effort needed to carry out an LCA and therefore facilitate the integration into the building process. A review of current industry practice and scientific literature shows two main approaches to address BIM-LCA integration. Either the LCA is performed in a simplified way at the beginning of the building process, or it is done at the very end when all the needed information is available, but it is too late for decision-making. One reason for this is the lack of methods, workflows and tools to implement BIM-LCA integration over the entire building process. Therefore, the main objective of this study is to develop an integrated BIM-LCA workflow implemented into a method for the whole building process using an existing structure for cost estimation. A tool is created and used in a case study in Switzerland to test the developed approach. The results of this study show that LCA can be performed continuously in each building phase over the entire building process using existing BIM modelling techniques. The main benefit of this approach is that the re-work caused by the need for re-entering data and the usage of many different software tools that characterise most of the current LCA practices is minimised. Furthermore, decision-making, both at the element and building levels, is supported.

Efficient fixed crane position analysis is critical in precast buildings construction to maximize lifting efficiency for precast elements. The primary objective is to minimize the travel distances of fixed cranes, thereby enhancing operational efficiency and cost control. This involves strategically reducing the distances between the transport truck parking, where elements are stored, the fixed crane, and the construction installation point. Despite its significance, existing studies often overlook the dynamic movement efficiency of transport trucks, particularly when dealing with multiple transport trucks and various models of fixed cranes, rendering the opti-mization of this scenario a challenging NP-hard problem susceptible to combinatorial explosion. This study presents a novel mathematical model leveraging the Genetic Algorithm (GA). The model aims to solve optimal model and position of fixed cranes, along with identifying ideal positions for transport truck parking. The study's methodology is validated and demonstrated through a project case study. Comparative analyses between the proposed model and the original planning underscore a noteworthy 6% reduction in both duration and costs when lifting elements directly from transport trucks, as opposed to establishing an on-site stock yard. Furthermore, the developed GA exhibits a substantial improvement in final solutions, achieving a remarkable 30% reduction in both operational duration and costs. This research contributes valuable insights into fixed crane position analysis of precast building construction, providing foundation for enhanced operational efficiency and cost-effectiveness in the industry.

This study explores the transformative potential of integrating Building Information Modeling (BIM) and Generative Design methodologies in heritage conservation and building management. By utilizing BIM, detailed architectural, structural, and MEP models were created, facilitating precise design and effective stakeholder collaboration. Generative Design enabled the exploration of multiple design solutions, optimizing spatial layouts and structural integrity. The project also integrated automated management systems and IoT sensors to enhance real-time monitoring, energy efficiency, and user comfort through the development of a digital twin. Despite encountering challenges such as technical complexities and budget constraints, the project successfully preserved the cinema's historical essence while incorporating modern functionalities. The findings highlight the contributions of BIM and Generative Design to the AEC industry, emphasizing their role in improving design accuracy, operational efficiency, and sustainability. This research provides valuable insights for future projects in heritage conservation, offering a blueprint for balancing historical preservation with contemporary needs. The revitalization of the "Ex Cinema Santa Barbara" in Paternò exemplifies these advancements, demonstrating how these technologies can restore and modernize culturally significant historical buildings effectively.

In the context of seismic risk, studying the characteristics of urban soils and of the built environment means adopting a holistic vision of the city taking a step forward compared to the current microzonation approach. Based on this principle, CLARA WebGIS aims to collect, organise, and disseminate the available information on soils and buildings in the urban area of Matera. The geodatabase is populated with i) 488 downloadable geological, geotechnical, geophysical, surveys; ii) geological, geomorphological, and seismic homogeneous microzones maps and iii) a new Digital Surface Model. The CLARA WebGIS (https://smartcities-matera-clara.imaa.cnr.it/) is the first publicly available database reporting for the whole urban area the spatial distribution of the fundamental frequencies for soils and the overlying 4043 buildings, along with probability levels of soil-building resonance. The WebGIS is addressed to a broad target of end users (local government, engineers, and geologists, etc.) as a support to the implementation of seismic risk mitigation strategies in terms of urban planning, seismic retrofitting, and management of post-earthquake crises. We recommend that the database be managed by local administrators, who would also have the task of deciding on future developments and continuous updating as new data becomes available.

With the development of bioinspired green solutions for sustainable construction over the past two decades, bio-cementation, which exploits the naturally occurring phenomenon of calcium carbonate precipitation in different environments, has drawn a lot of attention in both building construction and soil stabilization. Various types of microorganisms, along with specific enzymes derived from these microorganisms, have been utilized to harness the benefits of bio-cementation. Different application methods for incorporating this mechanism into the production process of the construction material, as well as a variety of experimental techniques for characterizing the outcomes of bio-cementation, have been developed and tested. Despite the success of bio-cementation as a sustainable method to construction has been demonstrated in a significant body of literature at the laboratory scale, the expansion of this strategy to construction sites and field application remains a pending subject. The issue may be attributed to two primary challenges. Firstly, the complexity of the bio-cementation phenomenon is influenced by a variety of factors. Secondly, the extensive body of literature examines various types of microorganisms under different conditions, leading to a wide range of outcomes. Hence, this study aims to examine the recent advancements in utilizing the most commonly employed microorganism, Sporosarcina Pasteurii, to emphasize the significance of influential factors identified in the literature, discuss the findings that have been brought to light, and outline future research directions toward scaling up the process.

As in every workplace, human sustainability in dental enterprises is connected to air and water quality, eco-friendly and naturally designed working spaces and 4R’s culture. Purpose of this study is to access the preferences and knowledge on circular economy and green building construction in a sample of students and dentists in Greece. Students (N1=93) and dentists (N2= 126) filled in e-questionnaires from April to December 2022. The response rate was 17,88% for N1 and 1.94% for N2. Data revealed that both students and dentists lack knowledge on circular economy (N1=67.7%, N2=68,25%), EU regulations on amalgam disposal (N1=64.5%, N2=58.73%) and plastic recycling (N1=76.34%, N2=76,98) while they do recycle at home (N1= 80.6%, N2=82.54%) and have participated in voluntary environmental actions (N1=58.1%, N2=66.67%). Gender influences the importance of factors related to green dental practices, with women students being more prone to answer positively as important for increase costs for network changes (p=0.02), lack of environmental awareness of manufacturers (p=0.057), and poor wastewater management (p=0.01). Students from urban areas are more prone to answer positively for lack of state financial support (p=0.02), low level of green design in the building (p=0.03), low direct financial benefit of a green dental office (p=0.04), low benefit to the reputation of the green dental office (p=0.02), lack of continuing education training seminars on green dentistry (p=0.05). For dentists, no significant relationships were observed except of a weak positive relationship in increase in costs for changes related to utility networks (p=0.08) while increased wasteful energy (p=0.12) and waste of dental materials (p=0.19) seemed important only for dentists in urban areas. Women dentists were more prone to answer positively on wasting energy (p=0.024) and use of unapproved disinfection products (p=0.036). The findings contribute ideas and solutions on green dental office buildings and sustainable behaviors through educational activities and social perspective of factors such as age, experience in dentistry, gender, and urbanism. It also inspires future multi-disciplinary research on dental quality assurance, psychology of environmentalism, economics, and behavioral science in dentistry.

In the recent decade, Building Information Modelling (BIM) has widely been adopted in the Architecture, Engineering and Construction (AEC) industry and completely upended the way we build. While BIM continues gain momentum in the industry, it has also attracted increasing attentions from researchers. However, most of the current study focuses on reviewing BIM for management, BIM for green building, BIM for infrastructure and BIM for Facilities Management (FM). There are few studies about Global BIM review and to discuss their complex inter-connections. In this study, we adopted a scientometric analysis method to review global BIM research from 2004-2019. A total of 1455 scholarly bibliographic records obtained from Web of Science Core Collection databased were established for the analysis. This study has identified the top productive and influential researchers, research institutes, regions/countries, subject categories and journals in the BIM field. In addition, 11 clusters of Global BIM research were also identified including construction project, green BIM, construction safety planning, automated IFC-based workflow and so on. Authors distinguished 11 clusters of global BIM research into 3 stages, namely formulating stage, accelerating stage and transforming stage. Furthermore, authors reviewed the BIM policy of Singapore and observed there is a co-production relationship between evolution of BIM policy and global BIM research. These findings provide valuable information for researchers, practitioners and policy makers by visualizing the current progress in the research field of BIM and highlighting future research needs.

During the MoDeCo2000 scientific and research project on mortars used in the territory of the Roman Danube Limes in Serbia, the biggest challenge was the quest for the provenance of used raw materials. The area where the largest city in the province of Moesia Superior developed, with millennial continuity of land use and settlement, was selected as a case study for deeper research. The material and immaterial values of Roman Viminacium have survived in the later life of the landscape, through the preserved building remains and artifacts, secondary use of building materials, but also toponyms, customs, and stories. Recycling of materials is commonly recognised in the modern age as the industrial processing of existing products in order to obtain raw materials and later prepare new products, representing one of the basic elements of sustainability. However, people throughout history have always used what they had at hand and the building remains were reused, but also recycled for new constructions. In this study we follow the presence of the specific material we call natural brick in the historical Viminacium landscape, focusing on Roman construction and specifically its potential use in lime mortars, connecting humanistic and natural sciences.

Air change rate is an important parameter for quantification of ventilation heat losses and also affects the indoor climate of buildings. Indoor air quality is significantly associated with ventilation. If air change isn't sufficient, trapped allergens, pollutants and irritants can degrade the indoor air quality and affect the well-being of a building's occupants. Many studies on ventilation and health have concluded that lower air change rates can have a negative effect on people’s health and low ventilation may result in an increase in allergic diseases. Quantification of air change rate is complicated, since it is affected by a number of parameters, of which the one of the most variable is the air-wind flow. This study aims to determination and comparison of values of the air change rate in two methods - by quantifying of aerodynamic coefficient Cp = Cpe - Cpi – so called aerodynamic quantification of the building and the methodology based on experimental measurements of carbon dioxide in the selected reference room in apartment building.

Human-generated, natural occurrences and other actions connected with the petroleum industry have contributed expansively to the contamination of numerous areas of human habitant, thus effecting overwhelming challenges and constraints to sustainable human health, development and the biosphere. The sites commonly associated include derelict pits, hydrocarbon onshore release sites, and places to where oil slicks exuding from offshore releases are blown onshore. In diverse cases, remediation is pertinent to restore the affected ambient. This is required despite the complexity and intricate pore structure and fluid trafficking trajectories of soil which are not easily amenable to remediation. Due to the complex assemblage of contaminated soil, it is necessary to conduct an encompassing site assessment by considering the potential impact of the environment and human health prior to proper selecting and implementing of a desirable remediation process. There are extant remediation methodologies which are effective and efficient for the clean up of contaminated shorelines and other petroleum-contaminated sites involving inter alia agricultural activities. There are salient variations in the techniques to expunge contaminants regarding spatiotemporal and pecuniary costs or considerations due to the gross environmental hazards entailed. This study provides for the opportunity to harness and sustain the capacity for the mitigation of untoward impacts, and induce the latitude for an enabling condition in sustainable human health, environment and development in the petroleum industry and other anthropogenic activities.

Building integrated photovoltaics (BIPV) consists of PV panels that are integrated into the building as part of the construction. This technology has advantages such as the production of electricity without necessary additional land area. This paper provides a literature review about the recent developments in urban building energy modelling, including tools and methods, and how they can be used to predict the effect of PV systems on building outdoor and indoor environments. It is also intended to provide a critical analysis on how PV systems affect the urban environment, both from an energy and a comfort point of view. The microclimate, namelly the urban heat island concept, is introduced and related with the existence of PV systems. It is concluded that urban building energy modelling (UBEM) can be effective to study the performance of PV systems in the urban environment. It allows to simultaneously predict the building energy performance and the microclimate effect. However, there is a need to develop new methodologies to overcome the challenges associated to UBEM, especially in what concerns non-geometric data, which leads to a major source of errors. and to find an effective method to predict the effect of PV systems in the urban environment.

Heating stage accounts for the largest proportion of building carbon emissions. In order to figure out heating situations of rural residential building in Gansu Province, Gannan region was selected to carry out on-site survey on heating condition including heating mode, energy used for heating, heating fees, residents’ satisfaction on heating and thermal environment of typical building. The results showed that local rural residents burnt scattered coal for heating with primitive heating stove with high heating cost and low efficiency, causing low air temperature and high heating fees. Besides carbon emissions caused by heating reached 5743.28 kgCO2em-2, Several strategies for improving the heating effect and reducing carbon were proposed taking economic benefits into consideration limited by rural economic development. A parameter of reduced carbon emissions per investment input was proposed to evaluate the carbon reduction strategies. Results showed that Biomass was the most economical way to reduce the carbon emissions, followed by thermal insulation design, natural gas furnace and air source heat pumps for heating, and it was not reasonable to blindly increase building insulation to reduce building carbon emissions. The optimum energy efficiency was 55% in Gannan. The results provided reference for building low-carbon design in rural areas, helped achieving low carbon goal with low investment.

This study reports an empirical analysis of an all-electric, Net Zero Energy Housing (NZEH) development located in a mixed-humid climate zone (4A, Virginia, USA). Circuit-level energy monitors were used to measure energy consumption and energy production data (solar photovoltaic) at 1-hr intervals in six identical apartments over 24 months. The study employs a multi-step case study methodology to a) empirically evaluate energy consumption and production data, b) identify the temporal variability of energy consumption and production data at different time scales, c) understand the impact(s) of weather and human-building interaction on energy consumption and production, and d) synthesize the study’s “lessons learned” toward data-driven recommendations for future NZEH researchers and practitioners. The study found that the development’s net zero energy goal was achieved in three of six case units and that NZEH housing performance was more influenced by human-building interaction than weather variability. The analysis also found the solar photovoltaic (PV) performance to be reliable across the sampled units over the periods of measurement, suggesting that solar PV could be oversized as an approach to overcome verifiability in HBI and achieve NZEH performance goals.

This paper develops a novel energy universal service bus system (EUSBS) based on emerging energy Internet (E-net) technologies. This EUSBS is a unified identification and plug-and-play interface platform to which high penetration distributed energy and equipment (DEE), including photovoltaic (PV), fans, electric vehicle charging stations (EVCSs), energy storage equipment (ESE), and commercial and residential users (CRUs), can access in a coordinated control and optimized utilization mode. First, the functions design, overall framework and topology architecture design of the EUSBS are expounded, among which the EUSBS is mainly composed of a hardware system and a software platform. Moreover, several future application scenarios are presented. Then, the hardware part of EUSBS is designed and developed, including the framework design of this hardware subsystem, and development of the hardware equipment for PV access, fans access, EVCS access, ESE access, and CRU access. The hardware subsystem consists of smart socket, and household/floor/building concentrators. Based on this, the prototypes development of EUSBS hardware equipment is completely demonstrated. Third, the software part of the EUSBS is developed as a cloud service platform for electricity use data analysis of DEE. This software subsystem contains the power quality & energy efficiency analysis module, optimization control module, information and service module, and data monitoring and electricity behavior analysis module. Based on this design, the software interfaces are developed. Finally, an application study on energy management and optimization of a smart commercial building is conducted to evaluate the functions and practicality of this EUSBS. The EUSBS developed in this paper is able to overcome difficulties in big data collection and utilization on sides of distribution network and electricity utilization, and eventually implement a deep information-energy fusion and a friendly supply-demand interaction between the grid and users. This contribution presents a detailed and systematic development scheme of the EUSBS, and moreover, the laboratory prototypes of the hardware and software subsystems have been developed based on E-net technologies. This paper can provide some thoughts and suggestions for the research of active distribution network and comprehensive energy management and optimization in power systems, as well as references and guidance for researchers to carry out research regarding energy management, optimization and coordinated control of the smart buildings.

With the increasing demand for energy worldwide, researchers from different fields have been striving to improve the sustainability and proper utilization of energy resources. Passive radiative cooling, as a natural energy transport method, can achieve cooling without additional external energy input. This review provides a comprehensive examination of passive radiative cooling, including its fundamental theories and latest development. A particular emphasis is placed on the diverse range of fields where passive radiative cooling has been applied, notably including but not limited to construction and architecture. The current state of applications, potential challenges that may arise with wider adaption and promising research directions for each field are thoroughly discussed. This review underscores the universal applicability and feasibility of passive radiative cooling and contributes to its future development by identifying promising areas for further exploration.

In recent years, the need to make the built environment more resilient and adaptable to climate change has become increasingly evident. In Europe, this aspect concerns the vast majority of existing buildings, which present several deficiencies from the energy-efficiency point of view, considering they were designed before the introduction of modern energy codes. Nowadays, it is possible to retrofit existing buildings using advanced and high-efficient technologies such as Double Skin Façades (DSFs). The research aims to evaluate the use of properly designed DSFs for the energy restoration of existing buildings. In detail, various DSF configurations are applied to a residential building located in central Italy and investigated under present and future climate conditions, estimated through regional climate models. The results underline that all the analysed Double Skin Façades confirm to be a useful option, and, in particular, the Multi-Storey typology allows drastic energy needs decrement. Moreover, the general increase in temperatures and solar radiations could affect the building energy performance, and the insertion of DSF can mitigate the climate change effects, reducing the predicted energy consumption and ensuring better behaviours than the building in its original state.

Building fires are preventable incidents that have proven to be both deadly and costly. Addressing their root causes will lead to safer neighbourhoods for families and businesses to live and operate in. Multiple studies have established the effect of residents’ socioeconomic compositions on an area’s building fire rates; however, the existing model based on the classical stepwise regression procedure has several limitations. This paper aims to construct a more accurate predictive model of building fire rates based on a set of explanatory socioeconomic variables. In building the socioeconomic model, a backward elimination by Robust Final Predictor Error (RFPE) criterion is proposed to enhance the forecasting capability of the model. The proposed method has been implemented on the census data and the fire incident data of the South East Queensland region in Australia. A cross-validation was then conducted to assess the model’s accuracy. In addition, comparative analyses of other elimination criteria, such as p-value, adjusted R-squared, Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC) and predicted residual error sum of squared (PRESS), were conducted. The cross-validation analyses demonstrate that the proposed criterion is a more accurate predictive model based on a couple of goodness-of-fit measures. All in all, the RFPE equation was found to be a suitable criterion for the backward elimination procedure in the socioeconomic modelling of building fires.

The objective of the study was to investigate the gap between data and evidence-based decisions among healthcare professionals considering the enormous amount of individual and aggregate data collected. Our study assessed the capacity, skills, and knowledge of the Ministry of Health leadership staff to understand data management, analysis, utilization, and dissemination. Three key components were assessed: 1) Knowledge through true/false questions, 2) Level of Skill (and Competency) using a Likert scale, and 3) Understanding of Key Concepts and Tools based on a Likert scale. The 183 study respondents were diverse healthcare professionals from Kenya, Tanzania, and Rwanda. Majority of respondents had not received any training on data management, analysis, interpretation, and utilization techniques, further there was a significant difference between those who had received training versus those who had not(p=0.005). The respondents were competent in work-related experiences but lacked skills and knowledge on: data concepts and tools, study designs, and types of data analysis. These findings explain the gap between data management, analysis, utilization, and dissemination among health professional’s cadre. To enhance service delivery and optimal provision of health care, it is imperative to have all health care professionals receive a well-designed training on data management, analysis, interpretation, and utilization.

The main goal of the study is to assess the Decision criteria, and the determining factor for the sustainability of entrepreneurial startups in order to contribute towards social inclusion and capacity building. Both concepts are in the development phase and are the outcome of entrepreneurial ecosystem and individual behavior and traits. The current study observed the research problem as entrepreneurship and the entrepreneurial startups are the continuous phenomena required for every economy. The lack of an efficient ecosystem and incompetent trait of an entrepreneur drags the entrepreneurial startup to a failure. Therefore, an assessment on decision criteria and determining factors categorizing them with their importance may provide requisites to lead a successful entrepreneurial startup contributing to the social inclusion and capacity building. The study solved the research problem by statistical assessment of decision criteria and determining factors and categorizing them with their importance may provide requisites to lead a successful entrepreneurial startup. The research is built on research questions, objectives, conceptual model, and hypothesis which are tested based on the data collected. The collection of data was done through a survey questionnaire on a sample of established entrepreneurs. The study concludes that five components of decision criteria are Region, Competition, Funding Opportunities, Tax System, and Country Economic Situation, whereas the eight components Consciousness and Reliable, Pursuit Results, Flexibility, Stress Resistance, Skills Identification Exploitation Potential Market Opportunities, Leadership, Creativity and Innovation, and Delegation Decision Making of determining factor are required for a successful entrepreneurial startup to be able to work towards social inclusion and capacity building.

An inter- and transdisciplinary concept has been developed, focusing on the scaling of industrial circular construction using innovative compacted mineral mixtures (CMM) derived from various soil types (sand, silt, clay) and recycled mineral waste. The concept aims to accelerate the systemic transformation of the construction industry towards carbon neutrality by promoting the large-scale adoption and automation of CMM-based construction materials, which incorporate natural mineral components and recycled aggregates or industrial by-products. In close collaboration with international and domestic stakeholders in the construction sector, the concept explores the integration of various CMM-based construction methods for producing wall elements in conventional building construction. Leveraging a digital urban mining platform, the concept aims to standardize the production process and enable mass-scale production. The ultimate goal is to fully harness the potential of automated CMM-based wall elements as a fast, competitive, emission-free, and recyclable alternative to traditional masonry and concrete construction techniques. To achieve this objective, the concept draws upon the latest advances in soil mechanics, rheology, and automation and incorporates open-source digital platform technologies to enhance data accessibility, processing, and knowledge acquisition. This will bolster confidence in CMM-based technologies and facilitate their widespread adoption. The extraordinary transfer potential of this approach necessitates both basic and applied research. As such, the proposed transformative, inter- and transdisciplinary concept will be conducted and synthesized using a comprehensive, holistic, and transfer-oriented methodology.

In this article, the main building of the former Uto City Hall, which was severely damaged in the 2016 Kumamoto earthquake, is investigated as a case study for the retrofitting of an irregular Reinforced Concrete building using the base-isolation technique. Its peak response is predicted via mode-adaptive bidirectional pushover analysis (MABPA), which was originally proposed by the authors. In the prediction step of MABPA, the peaks of the first and second modal responses are predicted considering the energy balance during a half cycle of the structural response. The numerical analysis results show that the peak relative displacement can be properly predicted by MABPA. The results also show that the performance of the retrofitted building models is satisfactory for the ground motion considered in this study, including the recorded motions in the 2016 Kumamoto earthquake.

The residential built form, including open space, provides the physical environment for social interaction. Understanding urban open space, including semi-public and public domains, through the lens of physical accessibility and visual permeability can potentially facilitate the building of a sense of community contributing to a better quality of life. Using an inner-city suburb in Perth, Western Australia as a case study, this research explores the importance of physical accessibility patterns and visual permeability for socialising in semi-public and public domains, such as the front yard and the residential streets. It argues that maintaining a balance between public and private inter-relationship in inner city residential neighbourhoods is important for creating and maintaining a sense of community.

This paper undertakes a comprehensive review to underscore the central role of sustainability in the evaluation and innovation of jet grouting technologies. It meticulously traces the evolution of evaluation methodologies, emphasizing the imperative of sustainable practices throughout history. Traditional evaluation paradigms are examined alongside contemporary trends, with a particular focus on sustainability metrics and visualization techniques. Through an examination of innovative jet grouting technologies, this paper illustrates how sustainability principles have driven advancements aimed at improving the efficiency and environmental impact of jet grouting methods. Central to this review is an assessment of sustainability across multiple dimensions, including method applicability, installation and operational compatibility, and the performance of the resulting jet grouted piles. By emphasizing sustainability considerations, the paper highlights the intrinsic link between environmental integrity and technological innovation in the field of jet grouting. In addition, it discusses analytical evaluation methods designed to proactively address sustainability concerns prior to construction, thereby promoting a more environmentally responsible approach. Finally, this paper presents a pioneering evaluation framework that integrates real-time monitoring and visualization tools, allowing stakeholders to track sustainability milestones throughout the construction process. By emphasizing sustainability as a guiding principle, this review advocates for a paradigm shift toward more ecologically sound and resilient jet grouting practices.

Adopting Building Information Modeling (BIM) within the Architecture, Engineering, and Construction (AEC) industry presents an opportunity to address enduring challenges, including chronic productivity deficits and emerging imperatives such as cleaner production and sustainability. However, the pace of BIM implementation (BIMI) across European Union (EU) countries is uneven due to varying contexts and the inherent complexity of BIM adoption. Notably, Portugal lags in BIMI among these countries. Using the Portuguese context, the manuscript endeavours to identify the main barriers to BIMI by the late-adopting countries in the EU and, based on the dynamics of their relationships, design effective mitigation measures to lessen their impact and eventually leverage BIMI. A comprehensive list of barriers was compiled from the literature and ranked by experts through a Delphi survey, resulting in 15 critical barriers. Subsequently, an Interpretive Structural Modelling (ISM) model was constructed to elucidate the hierarchical relationships among these barriers, while a fuzzy Impact Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) analysis was employed to determine their driving and dependence powers. The resulting main barriers were the lack of evaluation and feedback mechanisms on BIMI, ignorance of BIM capabilities/benefits, scarcity of BIM-capable professionals, lack of experience and cooperation in BIMI, resistance to change, and lack of support from top management. Finally, experts formulated mitigation measures aimed at tackling these main barriers. These measures were designed to not only address each barrier individually but also to have a comprehensive and practical impact on the entire barrier system as a whole. These findings will assist researchers, policymakers, and practitioners in comprehending the barriers and hierarchical structures affecting BIMI in EU late-adopting countries.

In the Kingdom of Saudi Arabia (KSA), residential buildings’ energy consumption accounts for almost 50% of the building stock electricity consumption. The electricity generation consumes over one-third of the daily oil production. KSA was ranked as one of the highest countries in fossil fuel consumption per capita in 2014. Moreover, the KSA’s economy heavily relies on fossil fuel sources, namely oil reservoirs, whereby depletion will negatively affect the future development of the country. The total electricity consumption is annually growing by approximately 5-8%, which would lead to identical oil consumption to oil production in 2035. Currently, the KSA government is concerned to generate more renewable energy using large renewable energy plants. The government is investing in energy generation through renewable sources, by financing large scale photovoltaic farms to stop an economic crisis that may occur in 2035. The existing building stock consumes around 80% of the total current Saudi electricity that is generated. According to the Saudi energy efficiency report, the primary energy consumption per capita is over three times higher than the world average. Therefore, the residential buildings need further assessment as to their current energy consumption. This research used a survey to explore current user behaviour in residential buildings energy performance in the city of Jeddah, KSA. The findings of the survey showed: • The buildings thermal properties were found to be poorly designed • The majority of users within the buildings prefer a room temperature of below 24 °C, which requires a massive amount of cooling • Due to the climate conditions and the cultural aspects of KSA, housing units are occupied for more than 18 hours per day • An increase in user awareness has helped to slightly improve residential buildings energy efficiency Knowing the current high energy consumption sources and causes and being able to define available opportunities for further developments on building thermal properties enhancements and how to increase user awareness to reach self-sustaining buildings is essential.

In general, isolators and dampers used in seismically isolated buildings are designed to be isotropic in any horizontal direction. However, in the case of buildings with plan irregularities, their nonlinear responses depend on the direction of seismic loading. To discuss the influence of the angle of seismic incidence (ASI) on the nonlinear response of irregular building structures, it is important to define the angle of the critical axis of the horizontal ground motion. One possible choice is the “principal axis of ground motion” proposed by Arias (1970). However, because this principal axis is independent of the natural period of a structure, it could be complicated to use for seismically isolated structures with long natural periods. In this study, the influence of the ASI of long-period pulse-like seismic input on an irregular base-isolated building is investigated. First, the angle of the principal axis of ground motion is defined in terms of the cumulative energy input. Then, a nonlinear time-history analysis of a five-story irregular base-isolated building is performed using 10 long-period pulse-like ground motion records considering various ASIs. The results show that, compared with the principal axis of ground motion proposed by Arias, defining the principal axis of ground motion in terms of the cumulative energy input is more suitable for discussions concerning the influence of the ASI on the response of an irregular base-isolated building.

This paper describes a case study of ventilation as well as measured and perceived indoor air quality (IAQ) in a Finnish comprehensive school with a hybrid ventilation system and reported IAQ problems. An operational error was found when investigating the ventilation system that prevented air from coming into classrooms, except for short periods of high carbon dioxide (CO₂) concentrations. However, results indicated that hybrid ventilation system was able to provide adequate ventilation and sufficient IAQ once properly designed and maintained. After ventilation operation was improved, occupants reported less unpleasant odors and stuffy air. The amount of total volatile organic compounds (TVOC) and some single volatile organic compounds (VOCs) decreased. Indoor mycobiota was observed in settled dust in the classrooms, from which ventilation improvement eliminated the dominant, opportunistic human pathogen species Trichoderma citrinoviride found before improvement.

Changes in extreme weather patterns are expected under climate change. In this study, a risk assessment was conducted using 4 building damage history datasets and 33 weather datasets (precipitation, wind speed, snow, and temperature) from 230 regions in South Korea to quantitatively analyze and predict building damage caused by potential future natural disasters. Decision tree analysis was used to evaluate building damage risk in 230 regions. The decision tree model to determine the risk of flood, gale, and typhoon was generated, which excluded gales, with less damage. The weight (variable importance) and limit value (damage limit) of the weather variables ware derived using the decision tree model. Using these two factors, we assessed the building damage risk in 230 regions in South Korea until 2100. The number of regions at risk of flood damage increased by more than 30% in average. Conversely, regions at risk of snowfall damage decreased by more than 90%. The regions at risk of typhoons decreased by 57.5% on average, and the number of regions at high risk of typhoon damage increased by up to 62.5% in RCP 8.5. These results can be used as objective data to minimize future building damage throughout South Korea, representing the first step towards sustainable development in the region with respect to disaster response.

High-rise building machines (HBMs) play a critical role in the successful construction of super-high skyscrapers, providing essential support and ensuring safety. The HBM’s climbing system relies on a jacking mechanism consisting of several independent jacking cylinders. A reliable control system is imperative to maintain a smooth posture of the construction steel platform (SP) under the action of the jacking mechanism. This research introduces three multivariate time series neural network models—namely, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Temporal Convolutional Network (TCN)—to predict the posture of HBM. The models take pressure and stroke measurements from the jacking cylinders as inputs, and their outputs determine the levelness of the SP and the posture of the HBM at various climbing stages. The development and training of these neural networks are based on historical on-site data, with the predictions subjected to thorough comparative analysis. All proposed neural network models exhibit the capability to dynamically predict the posture of the HBM during the climbing process, using data from sensors. Notably, the GRU model shows better predictive performance.

Community-level wastewater monitoring for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA has demonstrated useful correlation with both coronavirus disease 2019 (COVID-19) case numbers and clinical testing positivity. Wastewater monitoring on college campuses has demonstrated promising predictive capacity for the presence and absence of COVID-19 cases. However, to date, such monitoring has largely relied upon composite or grab samples and reverse transcription quantitative PCR (RT-qPCR) techniques, which limits the accessibility and scalability of wastewater monitoring. In this study, we piloted a workflow that uses tampons as passive swabs for collection and reverse transcription loop-mediated isothermal amplification (RT-LAMP) to detect SARS-CoV-2 RNA in wastewater. Results for the developed workflow were available same day, with a time to result following tampon swab collection of approximately three hours. The RT-LAMP 95% limit of detection (76 gene copies reaction-1) was greater than RT-droplet digital PCR (ddPCR; 3.3 gene copies reaction-1). Nonetheless, during a building-level wastewater monitoring campaign conducted in the midst of weekly clinical testing of all students, the workflow demonstrated a same-day positive predictive value (PPV) of 33% and negative predictive value (NPV) of 80% for incident COVID-19 cases. The NPV is comparable to that reported by wastewater monitoring using RT-qPCR. These observations suggest that even with lower analytical sensitivity the tampon swab and RT-LAMP workflow offers a cost-effective and rapid approach that could be leveraged for scalable same-day building-level wastewater monitoring for COVID-19.

The benchmarking of hotel energy use comprehensively identifies the controllable and uncontrollable factors affecting energy performance, including building characteristics, management strategies, operations, and maintenance systems. Other factors include climatic conditions, floor areas, operating hours, occupancy rates, and guest populations. A benchmarking study on energy consumption patterns in significant hotels (each with less than 100 rooms and an average staff strength of 40 employees), situated in the university town of Nsukka (longitude 70 23' E, latitude 60 52' N), Nigeria, was performed using the data envelopment analysis (DEA) methodology. DEA, a linear programming technique that measures the relative performances of units, was chosen as a benchmarking methodology due to its ability to handle multiple inputs and outputs. Following a correlation test, energy use intensity, diesel consumption, and the number of employees were selected as the analysis inputs, while the occupancy rate was chosen as the output variable. Data on these variables spanning 12 months were collected using questionnaires, interviews, site visits, and oral conversations with hotel managers to ensure validity. Grid-supplied electricity accounted for most of the hotels' energy needs, followed by diesel used in generators. More than 70% of the electricity use was for HVAC. From the DEA, Hotel 3 (DMU H3) had a technical efficiency score of 1, whereas adjustments were recommended for improving the efficiency scores of the other hotels, which were deemed inefficient. DMU H7 had the lowest efficiency score (0.474) and the highest identified savings for electricity and diesel. The analysis also revealed that occupancy rates were generally low in the months of June and July, coinciding with the high rainfall season with its accompanying decline in outdoor activities. Consistent with this, electricity consumption was highest in the Christmas and Easter holiday months of December, January, and April following increased travel-related activities.

This qualitative study investigates the management of supplier risks in e-commerce through relationship management strategies, focusing on trust-building, communication practices, digital technology integration, risk assessment methodologies, challenges, innovation initiatives, and ethical considerations. The research aims to provide nuanced insights into how e-commerce companies navigate the complexities of global supply chains to mitigate operational, financial, and reputational risks associated with supplier relationships. Through semi-structured interviews with 20 key stakeholders in the e-commerce sector, data were collected and analyzed using thematic analysis. The findings underscored the foundational role of trust in establishing resilient supplier relationships, facilitated by transparent communication, consistent performance evaluation, and mutual respect for contractual obligations. Effective communication practices, supported by digital platforms and technologies such as AI, blockchain, IoT, and cloud computing, enhanced operational transparency and decision-making capabilities across supply chains. The study identified diverse approaches to risk assessment, from qualitative evaluations to quantitative models utilizing predictive analytics and scenario planning. However, participants highlighted challenges including geopolitical uncertainties, trade disruptions, regulatory changes, and cultural barriers, necessitating adaptive strategies and diversified sourcing options. Innovation initiatives such as joint product development and technology adoption were crucial in enhancing supply chain agility and competitive advantage. Ethical considerations emerged as a critical aspect, influencing supplier selection criteria, CSR initiatives, environmental sustainability practices, and labor standards compliance. Integrating ethical guidelines into supplier contracts reinforced corporate values and enhanced brand reputation. Overall, this study contributes practical implications for e-commerce practitioners seeking to enhance supply chain resilience, mitigate supplier risks, and sustain competitive advantage in a dynamic global marketplace.

Building Information Modeling (BIM) is undeniably the most important trend in the digitization of the construction sector in recent years. BIM models currently being built are extremely geometrically rich, that is, they are modeled at a high level of detail in terms of geometry. Thanks to object-oriented programming paradigms, BIM models include high-level relationships to ensure interactions between objects, rapid view generation and documentation. However, these models are not always equally rich in non-graphical data. This is true for parameters at the library object level, with which building object models are saturated, but also at the project, site, building or floor level according to the structure of the interoperable Industry Foundation Classes (IFC) format. For this reason, experimental work was undertaken on semantic enrichment in non-graphical data of a public building (Public Kindergarten, Secemin, Poland), which has its BIM model at a high level of geometric detail but is poor in non-graphical data. As a result of the research and development work, all levels of the IFC structure were saturated with non-graphical data, validated, and the possibilities of their use were shown from the perspective of the facility manager. The article contributes to the discussion on semantic enrichment from CAD3D to BIM by presenting a detailed process for entering non-graphical data into a BIM model.

The research work hereby presented, emerges from the urge to answer the well-known question of how the uncertainty of intermittent renewable sources affects the performance of a microgrid and how could we deal with it. More specifically, we want to evaluate what could be the impact in performance of a microgrid intended to serve a smart-building (powered by photovoltaic panels and with battery energy storage), when the uncertainty of the photovoltaic-production forecasts is considered in the energy management process. For this, several objectives (or services) are targeted based in a two-step (double-objective) energy management framework, that combines optimization-based and rule-based algorithms. The performance is evaluated based on some particular services proposed as performance indicators. Simulations are performed using data of a study-case microgrid (Drahi-Xnovation center, Ecole Polytechnique, France). The use of quantile forecasts (obtained with an analog-ensemble method) is tested as a mean to deal with (i.e. decrease) the uncertainty of the solar PV production. The proposed energy management framework is compared with basic reference strategies and the results show the superior performance of the former in almost all the services and forecasting scenarios proposed. The contrasting nature among some of the target services is one of the main conclusions of this work, as well as the different requirements in terms of forecasts when optimizing for different services and seasons of the year. This fact highlights the usefulness of the quantile forecasting approach, as a tool to deal with the intrinsic uncertainty of PV power production

Automated extraction of buildings from earth observation (EO) data is important for various applications, including updating of maps, risk assessment, urban planning, policy making. Combining data from different sensors such as high-resolution multispectral (HRI) and light detection and ranging (LiDAR) has shown great potential in building extraction. Deep learning (DL) is increasingly used in multimodal data fusion and urban object extraction. However, DL-based multimodal fusion networks may underperform due to insufficient learning of “joint features” from multiple sources and oversimplified approaches to fusing multimodal features. Recently, an hybrid attention-aware fusion network (HAFNet) has been proposed for building extraction from a dataset including co-located Very-High-Resolution (VHR) optical images and Light Detection And Ranging (LiDAR) joint data. The system reported good performances thanks to the adaptivity of the attention mechanism to the features of the information content of the three streams but suffered from model overparametrization, which inevitably leads to long training times and heavy computational load. In this paper the authors propose a restructuring of the scheme, which involved replacing VGG-16-like encoders with the recently proposed EfficientNet, whose advantages counteract exactly the issues found with the HAFNet scheme. The novel configuration was tested on multiple benchmark datasets, reporting great improvements in terms of processing times, and also in terms of accuracy. The new scheme, called HAFNetE (HAFNet with EfficientNet integration), appears indeed capable of achieving good results with less parameters, translating into better computational efficiency. Based on these findings, we can conclude that, given the current advancements in single-thread schemes, the classical multi-thread HAFNet scheme could be effectively transformed by the HAFNetE scheme by replacing VGG-16 with EfficientNet blocks on each single thread. The remarkable reduction achieved in computational requirements moves the system one step closer to on-board implementation in a possible, future “urban mapping” satellite constellation.

Over the last two decades, residential buildings have accounted for nearly 50 percent of total energy use in New Zealand. In order to reduce household energy use, the factors that influence energy use should be continuously monitored and managed. Building researchers and professionals have made efforts to investigate the factors that affect energy use. However, few have concentrated on the association between household energy use and the cost of residential buildings. This study examined the correlation between household energy use and residential building cost. Analysis of the correlation between energy use data and residential building cost indicated that residential building cost in the construction phase and energy use in the operation stage were significantly correlated. These findings suggest that correct monitoring of building costs can help to identify trends in energy use. Therefore, this study proposes a time series model for forecasting residential building costs of five categories of residential building (one-story house, two-story house, townhouse, apartment, retirement village) in New Zealand. The primary contribution of this paper is the identification of the close correlation between household energy use and residential building costs and provide a new area for optimize energy management.

This paper proposes an active-reactive power collaborative scheduling model with cluster division for the flexible distributed energy resources (DERs) of smart building system to resolve the high complexity of centralized optimal scheduling of massive dispersed DERs in the distribution network. Specifically, the optimization objective of each cluster is to minimize the operational cost, the power loss cost, and penalty cost for the flexibility deficiency, and the second-order cone-based branch flow method is utilized to convert the power flow equations into the linearized cone constraints, reducing the nonlinearity and heavy computation burden of the scheduling model. Customized virtual battery models for the building-integrated flexible DERs are developed to aggregate the power characteristics of flexible resources while quantifying their regulation capacities with time-shifting power and energy boundaries. Moreover, a cluster division algorithm considering the module degree index based on the electrical distance and flexible balance contribution index is formulated for cluster division to achieve the information exchange and energy interaction in the distribution network with high proportion of building-integrated flexible DERs. Comparative studies have demonstrated the superior performance of the proposed methodology on the economic merits and voltage regulation.

Significant global challenges, particularly in the areas of economics, society, and the environment, are posed by urbanization. In this researcher-led study, we look at how Jordan is trying to bring the Sustainable Development Goals (SDGs) to life in its capital, Amman, by bringing urban laws in line with these global goals. The paper finds legislative flaws that prevent SDG incorporation through a thorough analysis of the Amman Building and Regulation Act of 2019. Some of the main points covered are public participation, the use of renewable energy sources, sustainable construction practices, resilience in urban areas, and the management of natural resources. Although a lot of work has gone into it, the analysis shows that if Jordan wants to accomplish the 2030 Agenda and have sustainable urban growth in the future, it must overcome these institutional and legal barriers. As a model for other cities facing a similar challenge, Amman is being asked to lead the way in SDG localization through more public involvement, new laws, and innovative resource management.

The present article is dealing with the state of question about building-energy monitoring systems used for data collection to estimate the Heat Lost Coefficient (HLC) with existing methods and so determinate buildings’ Envelope Energy Performance (EEP). In addition, the data requirements of HLC estimation methods are related with commonly used methods for fault detection, calibration and supervision of energy monitoring systems in buildings. Based on an extended review of experimental tests since 1978, a qualitative and quantitative analysis of Monitoring and Controlling System (MCS) specifications has been carried out. Although most actual Buildings Automation Systems (BAS) may measure the required parameters, further research is still needed to ensure that these data are accurate enough to rigorously apply the HLC estimation methods.

When extract building from high resolution remote sensing image with meter/sub-meter accuracy, the shade of trees and interference of roads are the main factors of reducing the extraction accuracy. Proposed a Bayesian Convolutional Neural Networks(BCNET) model base on standard fully convolutional networks(FCN) to solve these problems. First take building with no shade or artificial removal of shade as Sample-A, woodland as Sample-B, road as Sample-C. Set up 3 sample libraries. Learn these sample libraries respectively, get their own set of feature vector; Mixture Gauss model these feature vector set, evaluate the conditional probability density function of mixture of noise object and roofs; Improve the standard FCN from the 2 aspect:(1) Introduce atrous convolution. (2) Take conditional probability density function as the activation function of the last convolution. Carry out experiment using unmanned aerial vehicle(UVA) image, the results show that BCNET model can effectively eliminate the influence of trees and roads, the building extraction accuracy can reach 97%.

A new method for wide-area urban roof assessment of suitability for solar photovoltaics is introduced and validated. Knowledge of roof geometry and physical features is essential for evaluation of the impact of multiple rooftop solar photovoltaic (PV) system installations on local electricity networks. This paper begins by reviewing and testing a range of existing techniques for identifying roof characteristics. It was found that no current method is capable of delivering accurate results with publicly available input data. Hence a different approach is developed, based on slope and aspect using LIDAR data, building footprint data, GIS tools and aerial photographs. It assesses each roof’s suitability for PV installation. That is, its properties should allow the installation of at least a minimum size photovoltaic system. In this way the minimum potential solar yield for region or city may be obtained. The accuracy of the new method is then established, by ground-truthing against a database of 886 household systems. This is the largest validation of a rooftop assessment method to date. The method is flexible with few prior assumptions. It is based on separate consideration of buildings and can therefore generate data for various PV scenarios and future analyses.

The utilization of primary school buildings is multifaceted, primarily due to the high occupancy density, varying thermal preferences among occupants, diverse indoor activities (such as walking, sports, and conversation), and a constant flow of individuals entering and exiting the building. This results in frequent opening and closing of external windows and doors and fluctuations in internal heat gain. Consequently, frequent interactions between the indoor and outdoor microenvironments lead to energy losses. This study conducts a comprehensive literature review on building energy loss stemming from occupant behavior and the interactions between indoor and outdoor microenvironments. Furthermore, it proposes a dynamic real-time monitoring system based on a computer data capture and visualization platform foundation for building energy loss. The research methods include data crawling, data association rule mining, and data association analysis. The research findings yield a universally applicable and informative building energy-saving design theory based on extensive data analysis. Additionally, the system presents information on occupants' behavior and the microclimate data of indoor and outdoor environments on a computer screen, facilitating human-machine communication and enabling timely adjustments to design strategies for new buildings and operation and maintenance strategies for existing buildings.

Building energy demand impacts a myriad of interconnected economic, societal, and environmental aspects. As a result, Buildings Energy Models (BEM) play an important role in the process of urban design and planning. While previous studies have investigated the effects of building interventions on energy efficiency, their applicability may be limited due to the BEM’s high computational complexity. This limits their ability to systematically study important aspects of energy demand on a large scale. The development of Machine Learning Models (MLM) allows to design the required detailed analysis and solutions, while reducing the computational burden, making MLM attractive for urban designers. The capability of MLM to generalize well for multiple contexts (in our case, multiple buildings) is a crucial contributor to their applicability. However, the validation process in a wider context is often overlooked, therefore its generalization capabilities are not quantified. In this paper, we present a framework to train and validate a surrogate model derived from a physics-based BEM. Our method employs a Multiple Linear Regression model to predict Energy Use Intensity (EUI) for office buildings in Singapore using 36 input parameters (covariates), based on a training dataset of 23,000 samples. Model validation is performed by comparing the results of the Surrogate Model (SM) to a widely used BEM for a sample of 120 buildings. Our results indicate that the SM has an accuracy of NRMSE of 13%, NMBE of −3.56%, and R2 of 0.92, which suggests it can effectively and accurately predict building EUI. We also conduct a sensitivity analysis, which indicates that the parameters associated with internal loads and internal space usage are the most influential. Additionally, we present a reduced order model trained with only the 11 most influential parameters, which exhibits negligible loss in accuracy compared to the full SM while providing reduced complexity. Finally, we demonstrate an application of our SM to evaluate energy efficiency under uncertainty scenarios. The analytically derived results indicate a potential reduction of EUI of offices in Singapore from 227kWh/m2 to 99kWh/m2 by altering the building parameters that were identified as most influential.

Kenya’s construction industry is experiencing exponential growth in tandem with construction trends globally. With the growth experienced in the industry, there has been significant growth in the economy and a 50% contribution to the world’s landfill waste and pollution to the adjacent environment. Globally, the construction industry is among the largest consumers of natural resources and significant contributors to pollution. This has given rise to high pressure to adopt environmentally sensitive approaches in the construction industry to present a competitive advantage. It is, therefore, crucial to address the need for sustainability measures for Landscape Architecture projects to increase knowledge and awareness, create incentives geared towards supporting sustainable practices, and leverage legal instruments and policies. Green construction practices and strategies have positively impacted the environment and the Kenyan construction industry’s social and economic performance. However, there are several barriers to the adoption and improvement of monitoring and evaluation of sustainable practices, thus slowing down the industry in taking charge of sustainability.

Global temperature rise due to hydrocarbon gases emission that are produced by generating the electrical power has a great attention by the researchers to reduce it till zero emission is successfully achieved. Sustainable energy source such as solar energy, wind, hydro-energy and sea wave energy are focal areas to replace the fossil fuel by clean energy. In this article, daylight is used to minimize the power consumption that required for indoor lighting using electric roller blind. Smart controller is designed to adjust the position of the roller blind stepper motor, and hence, adjust the roller blind opening, based on the preset light intensity, to achieve precise utilization of daylight inside the room. If the desired Lux is not achieved for any reason, the smart controller adjusts the LED circuit current to boost the light intensity to achieve precisely the desired Lux. Comprehensive test cases using MATLAB-Simulink is carried out to verify the performance of the proposed smart controller. Techno-economic analysis is introduced to evaluate the benefits of installing the controller. Summary and recommendation are given at the end.

In this research, Cellulose Nanofibers (NFC) modified with a eutectic of lauric acid (LA) was prepared as a new form-stable phase change material (NFC-LA). Thermal properties of this composite were investigated by Differential Scanning Calorimetry (DSC). The results revealed that the melting temperature and latent heat of NFC/LA were 21.56 °C and 88.5 J/g, respectively; and the super cooling degree for the NFC-LA composite decreased to 13.99 °C when compared to 20.28 °C of the pure lauric acid. Natural clay was purified and modified with Cetyltrimethyl ammonium bromide (CTAB) to prepare organoclay. Through FTIR spectra, we have confirmed that the clay was successfully modified. The PCM-composite was then added to the organoclay to obtain a new composite denoted NFC-LA-OC. this latter was added to cement and was investigated as a reinforcement material in cement mortars for thermal energy storage application. The prepared material can both solve the leakage problem associated to the phase change material, and reduce or even avoid the use of heating and air conditioning systems, which are energy-intensive systems, and therefore reduce energy consumption.

Energy consumption is a critical issue in the energy sector, and recent events such as the global energy crisis, costs, the need to reduce greenhouse emissions, and extreme weather conditions have increased the demand for energy efficiency. Accurately predicting energy consumption is one of the key steps to addressing inefficiency in energy consumption, and its optimization. In this regard, accurate predictions of energy consumption would not only help to minimize wastage, but also to save cost. In this article, we propose intelligent models using an ensemble of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) neural networks for predicting energy consumption in smart buildings. The proposed model outperforms other state-of-the-art deep learning models for predicting minute energy consumption, with a mean square error of 0.109. The proposed model also accurately captures latent trends in the data that other models struggle to capture. The results highlight the potential of using hybrid deep learning models for improved energy efficiency management in smart buildings.

Urban spatial perception critically influences human behavior and emotional reactions, emphasizing the necessity of aligning urban spaces with human needs for enhanced urban living. However, functionality-based categorization of urban architecture is prone to biases, stemming from disparities between objective mapping and subjective perception. These biases can result in urban planning and designs that fail to cater adequately to the needs and preferences of city residents, negatively impacting their quality of life and the city's overall functionality. In this study, we apply machine learning to elucidate these biases within urban spatial perception research, utilizing a three-step methodology: objective mapping, subjective perception analysis, and perception deviation assessment. Our findings reveal that machine learning can expose hidden patterns within this research field, bearing substantial implications for urban planning and design. Of particular note, the study revealed significant discrepancies in the distribution centroids between commercial buildings and residential or public buildings. This result illuminates the spatial organization characteristics of urban architectural functions, serving as a valuable reference for urban planning and development. Moreover, it uncovers the advantages and disadvantages of different data sources and techniques in interpreting urban spatial perception, paving the way for a more comprehensive understanding of the subject. These findings underscore the importance of integrating both objective mapping and subjective perspectives in urban architectural functionality classification.

Structural design optimization (SDO) plays a pivotal role in enhancing various aspects of construction projects, including design quality, cost-efficiency, safety, and structural reliability. Recent endeavors in academia and industry have sought to harness the potential of Building Information Modeling (BIM) and optimization algorithms to optimize SDO and improve design outcomes. This review paper aims to synthesize these efforts, shedding light on how SDO contributes to project coordination. Furthermore, the integration of sustainability considerations and the application of innovative technologies and optimization algorithms in SDO necessitate more interactive early-stage collaboration among project stakeholders. This study offers a comprehensive exploration of contemporary research in integrated SDO employing BIM and optimization algorithms. It commences with an exploratory investigation, employing both qualitative and quantitative analysis techniques following the PRISMA systematic review methodology. Subsequently, an open-ended opinion survey was conducted among construction industry professionals in Europe. This survey yields valuable insights into the coordination challenges and potential solutions arising from technological shifts and interoperability concerns associated with widespread SDO implementation. These preliminary steps of systematic review and industry survey furnish a robust knowledge foundation, enabling the proposal of an intelligent framework for automating early-stage sustainable structural design optimization (ESSDO) within the construction sector. The framework ESSDO addresses the challenges of fragmented collaboration between architects and structural engineers. This proposed framework seamlessly integrates with the BIM platform, i.e., Autodesk Revit for architects. It extracts crucial architectural data and transfers it to the structural design and analysis platform, i.e., Autodesk Robot Structural Analysis (RSA), for structural engineers via the visual programming tool Dynamo. Once the optimization occurs, optimal outcomes are visualized within BIM environments. This visualization elevates interactive collaborations between architects and engineers, facilitating automation throughout the workflow and smoother information exchange.

Mercury has a strong environmental impact since both its organic and inorganic forms are toxic and it represents a pollutant of global concern. Liquid Hg is highly volatile and it can be released during natural and anthropogenic processes in the hydrosphere, biosphere and atmosphere. In this study the distribution of Gaseous Elemental Mercury (GEM) and the total and leached mercury concentrations on paints, plasters, roof tiles, concretes, metals, dust and wood structures were determined in the main buildings and structures of the former Hg-mining area of Abbadia San Salvatore (Siena, Central Italy). The mining complex (divided into 7 units) covers a surface of about 65 ha and contains mining structures and managers and workers buildings. In this work, nine surveys of GEM measurements were carried out from July 2011 to August 2015 for the buildings and structures located in the units 2, 3 and 6. Moreover, detailed measurements were performed in February, April, July, September and December 2016 in the edifices and mining structures of Unit 6. GEM concentrations showed a strong variability in terms of space and time mostly depending on the distance from the building hosting driers, furnaces and condensers and ambient temperature, respectively. In the Unit 2 surveys carried out in the hotter period (from June to September) showed GEM concentrations up to 27,500 ng m⁻³, while in the Unit 6 they were on average much higher and occasionally they saturated the GEM measurement device (>50,000 ng m⁻³). Concentrations of total (in mg kg⁻¹) and leached (in μg L⁻¹) mercury measured in different building materials (up to 46,580 mg kg⁻¹ and 4,470 mg L⁻¹ for total and leached mercury, respectively) showed for the same type of material highly variable values in dependence on the edifice or mining structure from which they were collected. The results obtained in this study are of relevant interest for the operational cleanings to be carried out during the reclamation activities.

Steel damper columns (SDCs) are energy-dissipating members that are suitable for reinforced concrete (RC) buildings and are often used for multistory housing. The evaluation of the peak deformation and hysteretic dissipated energy of such building structures is essential for the rational seismic design of RC buildings with SDCs. In a previous study, the authors proposed an energy-based prediction procedure for the peak and cumulative response of an RC frame building with SDCs. In this procedure, the accuracy of the equivalent velocity of the maximum momentary input energy (V_ΔE1^*)–peak equivalent displacement (D₁^*_max) relationship is essential for high quality predictions. In this article, the V_ΔE1^*–D₁^*_max relationships of RC moment-resisting frames with and without SDCs are invested using a critical pseudo-double impulse (PDI) analysis based on a study by Takewaki and coauthors. The results show that the V_ΔE1^*–D₁^*_max relationship obtained from the critical PDI analysis agrees well with that calculated from the equations proposed in the previous study.

The response of structures under pulse-like ground motions is characterized by the large amount of energy input in a few cycles. Consequently, structures with insufficient strength may suffer severe damage owing to excessive deformation. In a previous paper, the energy-based prediction procedure for the peak and cumulative response of a reinforced concrete (RC) frame building with steel damper columns was proposed (Fujii and Shioda, 2023). Although this procedure was verified by comparison to the nonlinear time-history analysis (NTHA) results, the performance of the proposed procedure with pulse-like ground motion records has not been verified yet. In this study, the accuracy of the energy-based prediction procedure for an RC frame building with steel damper columns was investigated by considering pulse-like ground motions. The numerical analysis results reveal that the accuracy of the predicted peak response is satisfactory, which agrees with the results of the author’s previous study. However, the accuracy of the predicted total input energy to the building model depends on the ratio of the pulse period of the ground motion to the effective fundamental period of the building model. The reasons for this underestimation of the total input energy are discussed in this paper.

Only now, four decades after passive houses were designed in the US and built in Canada, the authors are able to discuss the next generation of new and retrofitted buildings in the same way. This paper presents a universal system for different types of buildings and climates that includes construction experience and is reinforcing multi-disciplinary synergies. A next generation of technology uses adaptable indoor climate, integrates HVAC and building structure; applies field monitoring of energy and indoor environment and develops a performance model that is based on artificial neutral network. This approach, includes: 1. The self-learning ANN as a part of the building management system, 2. This management system guides energy optimization in a post-construction stage 3. A two-stage construction process for new and retrofits. The first stage prescribes investment level and optimizes performance; opposite in the second stage. This paper highlights that some elements of the proposed methodology have already been applied in the practice. It also underlines that this integrated multi-disciplinary system can be applied with a different combination of technological elements.

In China's mountainous coastal terrain, the storm badly damaged low-rise buildings. At present, it is not clear how the relative position of buildings and mountains affects the surface of a low-rise building’s fluctuating coefficient. The study compared these results with the wind pressure distribution without the surrounding environment. The primary objective of this study is to examine the fluctuating coefficient variation rule based on the spacing between hillsides and buildings under 0° wind angle. Additionally, the study analyzes the fluctuating wind pressure coefficient across wind angles between 0°~90°. The distribution of wind pressure in different common hillside landforms was examined through a wind tunnel experiment, which also compared with the distribution in an open environment. The study examined the fluctuating coefficient as the distance between the building and the hillside changed, specifically for wind blowing at a 0° angle. Next, an analysis is conducted on the changing pattern of the fluctuating coefficient for low-rise buildings ranging from a wind angle of 0° to 90°. Simultaneously, the investigation examined the power spectrum and wind pressure’s probability distribution, while considering the proximity of the building to an adjacent hill. The findings indicated that the changing wind pressure factor on the surface of the structure differed based on the relative location of the hillside and the building. The roof's wind pressure coefficient fluctuated and gradually increased until it reached its peak, unaffected by the surroundings. The low-frequency energy decreased in windward eaves, while the high-frequency energy progressively rose as the distance between the hillside and the building increased. The wind pressure on the leeward side exhibited Gaussian characteristics in its probability distribution.

Engineer-to-order manufacturing, characterized by highly customized products and complex workflows, presents unique challenges for warehouse management and operational efficiency. This paper explores the potential of a digital twin as a transformative solution for engineer-to-order environments in manufacturing companies realizing prefabricated building components. The paper outlines a methodology encompassing users’ requirement and the design to support development of a digital twin that integrates Internet of Things devices, Building Information Modeling, and Artificial Intelligence capabilities. It delves into the specific challenges of outdoor warehouse optimization and worker safety within the context of engineer-to-order manufacturing, and how the digital twin aims to address these issues through data collection, analysis, and visualization. The research is conducted through an in-depth analysis of the warehouse of Focchi S.p.A., a leading manufacturer of high-tech prefabricated building envelopes. Focchi's production processes and stakeholder interactions are investigated, and the paper identifies key user groups and their multiple requirements for the warehouse improvement. It also examines the potential of the digital twin to streamline communication, improve decision-making, and enhance safety protocols. While preliminary testing results are not yet available, the paper concludes by underlining the significant opportunities offered by a BIM, IoT, and AI-powered Digital Twin for engineer-to-order manufacturers. The research, developed within the IRIS project serves as a promising model for integrating digital technologies into complex warehouse environments, paving the way for increased efficiency, safety, and ultimately, a competitive edge in the market of manufacturing companies working in construction industry.

This paper presents a methodology for the integration of Building Information Modeling (BIM) and Life Cycle Cost Analysis (LCCA) to assess the economic implications of designing aging-in-place (AIP) homes at the conceptual stage. With the global increase in the aging population, there is an increased demand for housing solutions tailored to the needs of elderly individuals. Focusing on the importance of the early phase of design, this study aims to improve the process of making efficient decisions by providing a comprehensive assessment of the life cycle costs associated with AIP homes. The study introduces a semi-automated model for the economic evaluation of AIP homes, enabling the estimation of costs throughout the houses’ entire life cycle, from design and construction to operation, maintenance, and eventual renovation or disposal. The said model facilitates the exploration of the long-term economic feasibility of design’s related decisions with an emphasis on the importance of considering the life cycle costs early during the design process to optimize the functionality and economic viability. By investing in accessible and universal design features upfront, the initial costs for modifications can lead to long-term savings by reducing the need for extensive retrofits. The model can easily do comparison between different design alternatives in terms of their lifecycle costs, allowing designers to assess the financial impact of using important features in their design such as wider doorways, accessible bathrooms, and elevators. Overall, this study provides valuable insights for designers and homeowners about the economic aspects of designing AIP homes as a support for efficient decision-making during the early stages of the design process.

This paper is an extension to previous work that introduced successful classical-control for indoor lighting system that simultaneously control LED system and roller curtain to achieve accurate light intensity for indoor lighting applications. In this paper, Multi Input Multi Output Fuzzy Logic Controller (MIMO FLC) is proposed to replace the classical-controller. Detailed method is provided to illustrate the steps to design the required MIMO FLC that has two fuzzy input, two fuzzy output and forty-five fuzzy rules with mamdani fuzzy engine. The proposed MIMO FLC is simulated in Matlab using Simulink tools. The results are illustrated, analyzed and compared with the classical-control. The response showed very close performance to the classical-controller. The maximum average error and the maximum error with respect to “Desired Lux” inside the room is -6% and -10% respectively.

Dealing with and maintaining high-quality standards in the design and construction phases is challenging, especially for on-site construction. Issues like improper implementation of building components and poor communication can widen the gap between design specifications and actual conditions. To prevent this, particularly for energy-efficient buildings, it's vital to develop resilient, sustainable strategies. These should optimize resource use, minimize environmental impact, and enhance livability, contributing to carbon neutrality by 2050 and climate change mitigation. Traditional post-occupancy evaluations, which identify defects after construction, are impractical for addressing energy performance gaps. A new, real-time inspection approach is necessary throughout the construction process. This paper suggests an innovative guideline for prefabricated buildings, emphasizing digital 'self-instruction' and 'self-inspection'. These procedures ensure activities impacting quality adhere to specific instructions, drawings, and 3D models, incorporating relevant acceptance criteria to verify completion. This methodology, promoting alignment with planned energy-efficient features, is supported by BIM-based software and Augmented Reality (AR) tools, embodying Industry 4.0 principles. BIM and AR bridge the gap between virtual design and actual construction, improving stakeholder communication and enabling real-time monitoring and adjustments. This integration fosters accuracy and efficiency, key for energy-efficient and Nearly Zero-Energy Buildings, marking a shift towards a more precise, collaborative, and environmentally sensible construction industry.

The Covid-19 pandemic and the subsequent implementation of lockdown measures have significantly impacted global electricity consumption, necessitating accurate energy consumption forecasts for optimal energy generation and distribution during a pandemic. In this study, we propose a new forecasting model called the Multivariate Multilayered LSTM with Covid-19 case injection ($\proposedModel$) for improved energy forecast during the next occurrence of a similar pandemic. We utilize data from commercial buildings in Melbourne, Australia during the Covid-19 pandemic to predict energy consumption and evaluate the model's performance against commonly used methods such as LSTM, Bi-LSTM, Linear Regression, Support Vector Machine and the previously published work of Multilayered LSTM (M-LSTM). The proposed forecasting model was analyzed using the following metrics of mean percent absolute error (MPAE), normalized root mean square error (NRMSE), and $R^2$ score values. The model $\proposedModel$ demonstrates superior performance, achieving the lowest MPAE values of 0.061, 0.093, and 0.158 for data sets from 3 different buildings, respectively. Our results highlight the improved precision and accuracy of the model, providing valuable information for energy management and decision-making during the challenges posed by the occurrence of a pandemic like Covid-19 in the future.

This article is a continuity of prevouse two reserch in daylight harvastion control that were deigned using classical control apoproach and then fuzzy logic technique, In this article ANN controller is designed and its response is compared with the performance of the other two controllers. Detailed compression for the different error type is illustrated, however, the results show that the three controller operate with satisfactory accuracy.

With the advent of digitization, the integration of advanced technologies in operations and projects across all sectors is in progress. Accordingly, efforts are being made to utilize cutting-edge technologies to improve the circularity of the built environment. This study aimed to review the existing applications and limitations of building information modeling (BIM) tools for circular economy (CE) implementation from this perspective. A literature review was conducted to provide an overview of the use of BIM tools for conducting life cycle assessments, energy analysis, waste management, and formulation of material passports for buildings. It was found that BIM tools, which are available across all life cycle phases of building, have a great potential to improve the circularity of the sector. The overview provided on the use of various BIM tools may help stakeholders of the built environment understand the role of BIM for CE adoption in the sector.

This research raises questions about the possibilities and options of using BIM methodology for wood design and construction associated with software for structure modeling along the asset's cycle life. Likewise, several academic and research initiatives are reviewed. In this sense, this paper aims to establish an appropriate link between two agendas that the architecture, engineering, and construction (AEC) industry, academia, and governments normally handle separately. By conducting several literature reviews (book, journals, and congresses) and extensive software tests (BIM software: Revit, Archicad, Tekla, and Wood plug-ins: AGACAD, Archiframe, Timber Framing 2015, WoodStud Frame, etc.), the state-of-the-art was assessed in both fields, several cases linking BIM and wood are shown in detail and discussed, various theoretical samples are modelled and shown, and the advantages and disadvantages of each technique and stage are explained. On the other hand, although wood construction has been the most common for hundreds of years, this is not the case of BIM Modeling software developments associated with this materiality. Furthermore, since the appearance of materials such as steel and reinforced concrete, all software developments have focused on these materials, leaving aside the possibility of developing applications for use in wood projects. According to previously discussed, it can be concluded that BIM for Wood has been used more frequently in academia and that both fields have several common processes and, in many cases, only a few BIM-wood tools have been used. Thus, disregarding the high potential and high level of benefits that results with the application of these methodologies for the complete building life cycle (design, construction, and operation).

High-Rise buildings with their particular features can affects on surrounding environment and makes new microclimates. In the windy conditions, the spaces that are between building blocks changes to passages and affects on the wind velocity, intensity and it’s other parameters.The importance of this effect is different in each level of building height. The Pedestrian-Level is the lowest and one of important areas. Markets, playgrounds and pedestrian access had located in this area and any unwanted microclimate changes like high velocity and turbulence in this level can makes discomfort and dangerous condition for residents. So this research tries to consider the pedestrian- level wind comfort in some High-Rise building complexes arrangement that had located in Tehran district 22 with Computational Fluid Dynamics (CFD) modeling and reaching to a suitable arrangement pattern. It had collected the required data through field study and librarian databases and then compared them with standard guidelines and analyzed them by comparative comparison method. As a result a linear arrangement that placed crossover to wind direction for providing wind comfort and preventing wind danger is been suggested in this region.

Advancements in Parametric Design, Generative Design, and automation in Building Information Modelling (BIM) have opened new opportunities for architects and engineers working on complex buildings, such as offices. These developments allow designers to enhance their designs, increase project efficiency, improve performance, and reduce project time and costs. To address conflicting objectives that arise during the design process, genetic algorithms with multi-objective optimization (MOO) have been employed in architectural design. The GENIUS project aims to optimize building shape, apertures, and shading systems in the concept and design stages, with a focus on energy and daylight performance. The project integrates BIM and visual programming tools with Artificial Intelligence techniques like Genetic Algorithms and RBFOpt model-based optimization. Optimization algorithms are used to identify the best solutions that meet all design objectives, helping architects optimize their designs and achieve desired outcomes. The workflow was tested on a case study of a large office building, with MOO focused on maximizing daylight performance using Spatial Daylight Autonomy metric and minimizing energy consumption using Energy Use Intensity metric. The project provides architects with a method to improve their designs using Algorithm Aided Design tools, which help identify the best solutions for complex design problems.