Date of Award
12-2025
Document Type
Thesis - SCU Access Only
Publisher
Santa Clara : Santa Clara University, 2025
Degree Name
Master of Science (MS)
Department
Computer Science and Engineering
First Advisor
Yi Fang
Abstract
As sustainable construction continues to gain prominence, the industry’s increased emphasis on environmental responsibility and resource efficiency has spurred growing interest in advanced technologies and frameworks. Designing sustainable buildings involves the integration of diverse and interdependent strategies spanning building systems, lifecycle analyses, material selection, and stakeholder behavior. Retrieval-augmented generation (RAG) is a powerful artificial intelligence framework combining the strengths of information retrieval and generative models to enhance contextual understanding and knowledge accessibility. Despite its wide application in various domains, the potential of RAG remains largely unexplored in civil engineering contexts. To bridge this gap, this thesis introduces a tailored RAG framework designed specifically to address sustainability-related queries, significantly improving the efficiency of accessing specialized knowledge for industry practitioners. Leveraging a domain-specific corpus derived from reference materials within the Leadership in Energy and Environmental Design (LEED) rating system, the proposed framework enhances retrieval effectiveness and answer interpretability through the integration of structured knowledge graphs and refined prompt engineering. Iterative optimization of the retrieval and prompting methods further strengthens the system’s ability to handle complex, calculation-intensive queries that are prevalent in sustainable design scenarios. Experimental results demonstrate substantial improvements in both accuracy and practical usability, supporting more informed and reliable decision-making processes in sustainable building projects. Ultimately, this research highlights the transformative potential of AI-powered approaches for streamlining LEED compliance and advancing the field of sustainable construction.
Recommended Citation
Wang, Haisong, "A Retrieval-Augmented Generation Framework for LEED Compliance in Sustainable Building Design and Construction" (2025). Computer Science and Engineering Master's Theses. 59.
https://scholarcommons.scu.edu/cseng_mstr/59
