AI-Assisted Sustainability Assessment of Building Materials and Its Application in Green Architectural Design

Abstract

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.