Version 1
: Received: 27 August 2024 / Approved: 28 August 2024 / Online: 28 August 2024 (10:50:50 CEST)
How to cite:
Askarinejad, P.; Behnia, B. Decarbonizing Tall Building Structures: Implementing Machine Learning At The Early-stage Of Design Process. Preprints2024, 2024082029. https://doi.org/10.20944/preprints202408.2029.v1
Askarinejad, P.; Behnia, B. Decarbonizing Tall Building Structures: Implementing Machine Learning At The Early-stage Of Design Process. Preprints 2024, 2024082029. https://doi.org/10.20944/preprints202408.2029.v1
Askarinejad, P.; Behnia, B. Decarbonizing Tall Building Structures: Implementing Machine Learning At The Early-stage Of Design Process. Preprints2024, 2024082029. https://doi.org/10.20944/preprints202408.2029.v1
APA Style
Askarinejad, P., & Behnia, B. (2024). Decarbonizing Tall Building Structures: Implementing Machine Learning At The Early-stage Of Design Process. Preprints. https://doi.org/10.20944/preprints202408.2029.v1
Chicago/Turabian Style
Askarinejad, P. and Behzad Behnia. 2024 "Decarbonizing Tall Building Structures: Implementing Machine Learning At The Early-stage Of Design Process" Preprints. https://doi.org/10.20944/preprints202408.2029.v1
Abstract
The construction of tall buildings generates a high spatial and temporal concentration of greenhouse gas (GHG) emissions. Research studies have shown that as building height increases, more resources per floor area are required to withstand the increasing effects of lateral loads (wind and earthquake). This has major implications for the environmental performance of tall buildings since the Embodied GHG Emissions (EGHGE) of structural systems tend to represent the greatest portion of the life cycle GHG emissions of tall buildings. This study presents a data driven-based approach for decarbonization of tall buildings and evaluates the significant impact of material types (concrete, steel, and timber), concrete strength, and structural systems relative to a building's height on the EGHGE associated with tall buildings. In mitigating the effects of climate change, this research implements machine learning (ML) algorithms as an early-stage design tool to facilitate the choice of materials, and structural systems for tall buildings. This work considers a wide range of high-rise buildings with four different types of lateral structural systems (Braced-Frame system, Outrigger-Belt system, Shear Wall system, and Tubular system), four different types of construction materials (concrete, steel, hybrid, and timber), varying heights (ranging from 10 to 100 stories), and various concrete materials with different compressive strength ranging from 32 to 90 MPa. Data gathered from more than 100 existing tall building projects along with the data obtained from finite element analysis of 120 high-rise buildings models were utilized to train various ML regression algorithms: Decision Tree, Support Vector Machine, Polynomial Regression, and Elastic-Net Regularized Regression. The performance of ML models was carefully assessed, and the best prediction model was selected to estimate the total amount of CO2 emissions for high-rise buildings. Results indicate that hybrid structures with the Out-Rigger-Belt system exhibit the lowest carbon emission (110 kg/m2) compared to other structural types and systems.
Keywords
Decarbonization; Tall Buildings; Greenhouse Gas Emissions; Machine Learning; Finite Elements Analysis; Structural Systems
Subject
Engineering, Architecture, Building and Construction
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
