Version 1
: Received: 31 May 2024 / Approved: 31 May 2024 / Online: 31 May 2024 (10:53:49 CEST)
How to cite:
Adewale, B. A.; Ene, V. O.; Ogunbayo, B. F.; Aigbavboa, C. O. Application of Artificial Intelligence (AI) in Sustainable Building Lifecycle; A Systematic Literature Review. Preprints2024, 2024052113. https://doi.org/10.20944/preprints202405.2113.v1
Adewale, B. A.; Ene, V. O.; Ogunbayo, B. F.; Aigbavboa, C. O. Application of Artificial Intelligence (AI) in Sustainable Building Lifecycle; A Systematic Literature Review. Preprints 2024, 2024052113. https://doi.org/10.20944/preprints202405.2113.v1
Adewale, B. A.; Ene, V. O.; Ogunbayo, B. F.; Aigbavboa, C. O. Application of Artificial Intelligence (AI) in Sustainable Building Lifecycle; A Systematic Literature Review. Preprints2024, 2024052113. https://doi.org/10.20944/preprints202405.2113.v1
APA Style
Adewale, B. A., Ene, V. O., Ogunbayo, B. F., & Aigbavboa, C. O. (2024). Application of Artificial Intelligence (AI) in Sustainable Building Lifecycle; A Systematic Literature Review. Preprints. https://doi.org/10.20944/preprints202405.2113.v1
Chicago/Turabian Style
Adewale, B. A., Babatunde Fatai Ogunbayo and Clinton Ohis Aigbavboa. 2024 "Application of Artificial Intelligence (AI) in Sustainable Building Lifecycle; A Systematic Literature Review" Preprints. https://doi.org/10.20944/preprints202405.2113.v1
Abstract
With buildings accounting for a significant portion of global energy consumption and greenhouse gas emissions, the application of artificial intelligence (AI) holds promise for enhancing sustainability in the building lifecycle. This systematic literature review addresses the current understanding of AI's potential to optimize energy efficiency and minimize environmental impact in building design, construction, and operation. A comprehensive literature review and synthesis were conducted to identify AI technologies applicable to sustainable building practices, examine their influence, and analyze the challenges of implementation. The review was guided by a meticulous search strategy utilizing keywords related to AI application in sustainable building design, construction, and operation. The findings reveal AI's capabilities in optimizing energy efficiency through intelligent control systems, enabling predictive maintenance, and aiding design simulation. Advanced machine learning algorithms facilitate data-driven analysis and prediction, while digital twins provide real-time insights for informed decision-making. Furthermore, the review identifies barriers to AI adoption, including cost concerns, data security risks, and challenges in implementation. AI presents a transformative opportunity to enhance sustainability in the built environment, offering innovative solutions for energy optimization and environmentally conscious practices. However, addressing technical and practical challenges will be crucial for the successful integration of AI in sustainable building practices.
Keywords
Artificial Intelligence; Sustainability; Building Life Cycle; Design Optimization; Digital Twins; Internet of Things
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.
