A Systematic Review of the Applications of AI in a Sustainable Building’s Lifecycle

Систематический обзор применения искусственного интеллекта на протяжении жизненного цикла устойчивого здания
B. A. Adewale, Vincent Onyedikachi Ene, Babatunde Fatai Ogunbayo, Clinton Aigbavboa
2024-07-11

artificial intelligencedigital twinsenergy efficiency optimizationpredictive maintenancesustainable building lifecycle
Buildings significantly contribute to global energy consumption and greenhouse gas emissions. This systematic literature review explores the potential of artificial intelegence (AI) to enhance sustainability throughout a building’s lifecycle. The review identifies AI technologies applicable to sustainable building practices, examines their influence, and analyses implementation challenges. The findings reveal AI’s capabilities in optimising energy efficiency, enabling predictive maintenance, and aiding in design simulation. Advanced machine learning algorithms facilitate data-driven analysis, while digital twins provide real-time insights for decision-making. The review also identifies barriers to AI adoption, including cost concerns, data security risks, and implementation challenges. While AI offers innovative solutions for energy optimisation and environmentally conscious practices, addressing technical and practical challenges is crucial for its successful integration in sustainable building practices.
1
AI can enhance sustainability across a building’s lifecycle, particularly through energy-efficiency optimization, predictive maintenance, and design simulation.
2
Advanced machine-learning algorithms support data-driven analysis for sustainable building practices.
3
Digital twins provide real-time insights that can improve decision-making throughout building lifecycle management.
4
Major barriers to AI adoption include implementation difficulties, high costs, and data-security risks.
5
Successful integration of AI into sustainable buildings requires addressing both technical and practical challenges.

sustainable buildings throughout their lifecycle

applications of artificial intelligence for energy optimization, predictive maintenance, design simulation, and sustainable decision-making, including adoption barriers

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Publication Date
2024-07-11
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Authors
B. A. Adewale
Vincent Onyedikachi Ene
Babatunde Fatai Ogunbayo
Clinton Aigbavboa
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