Accepted 2025 July 31 ; Published 2025 August 7

Journal of Industrial Engineering and Applied Science, 2025, 3(4), 3005-6071.

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The construction industry faces unprecedented challenges in achieving sustainable development goals while maintaining economic viability and design excellence. This research presents a comprehensive AI-assisted framework for evaluating building material sustainability, addressing the critical gap between environmental consciousness and practical implementation in architectural design. The proposed methodology integrates machine learning algorithms with multi-criteria decision analysis to assess material properties including carbon footprint, durability, cost-effectiveness, and recyclability. Through extensive case studies comparing traditional and AI-assisted material selection processes, the framework demonstrates significant improvements in decision-making accuracy and environmental impact reduction. The system incorporates real-time data from global material databases and integrates seamlessly with existing Building Information Modeling tools. Results indicate a 34% improvement in sustainability scoring accuracy and 72% reduction in material selection time compared to conventional methods. The cross-cultural validation study reveals substantial differences between US and Chinese green building standards, highlighting the need for adaptive AI frameworks. This research contributes to the advancement of intelligent design methodologies and supports the transition toward sustainable construction practices in the era of Industry 4.0.

Artificial Intelligence , Building Materials , Sustainability Assessment , Green Architecture , BIM Integration .

APA Style

Xu, S. (2025). Ai-assisted sustainability assessment of building materials and its application in green architectural design. Journal of Industrial Engineering and Applied Science, 3(4), 1-13. https://doi.org/10.70393/6a69656173.333130

I would like to extend my sincere gratitude to N. Rane for their comprehensive research on integrating leading-edge artificial intelligence, internet of things, and big data technologies for smart and sustainable architecture, engineering and construction industry, as published in their article titled[2] "Integrating leading-edge artificial intelligence (AI), internet of things (IOT), and big data technologies for smart and sustainable architecture, engineering and construction (AEC) industry: Challenges and future directions" (2023). Their systematic analysis of AI integration challenges and technological frameworks has significantly influenced my understanding of intelligent building systems and provided valuable methodological insights for developing comprehensive AI-assisted sustainability assessment approaches in architectural design. I would like to express my heartfelt appreciation to X.Q. Wang, P. Chen, C.L. Chow, and D. Lau for their groundbreaking study on the artificial-intelligence-led revolution of construction materials, as published in their article titled[6] "Artificial-intelligence-led revolution of construction materials: From molecules to Industry 4.0" in Matter (2023). Their innovative exploration of AI applications across the entire construction material lifecycle, from molecular design to industrial implementation, has profoundly enhanced my knowledge of intelligent material development processes and inspired the development of machine learning algorithms for material property analysis presented in this research.

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