Intelligent BIM-Integrated Decision Support for Life Cycle Assessment Using Large Language Models

The construction industry has a profound impact on the environment, consuming significant amounts of energy, water, and raw materials, while also generating waste and greenhouse gas emissions. To mitigate these effects, it is crucial to adopt sustainable construction practices, which are often supported by environmental certification. A key component of these certifications is Life Cycle Assessment (LCA), a process that is frequently hampered by labor-intensive and time-consuming manual data handling. This study introduces an enhanced BIM-based decision support system that integrates LCA processes with advanced technologies, including Large Language Models (LLMs). The system automates the extraction, transfer, and analysis of environmental data from BIM models using tools like Revit, Dynamo, and LCA databases. The open-source model “Meta-Llama-3.1-8B” is utilized to analyze LCA results, recommend sustainable material alternatives, and facilitate automated scenario comparisons through trade-off analyses. These features enable real-time feedback, dynamic decision-making, and improved optimization of material selection for sustainability. The TR building at UPC serves as a case study to validate the system. The findings indicate significant efficiency gains, enhanced decision-making capabilities, and reduced environmental impacts. By incorporating LLMs, this study addresses existing gaps in BIM-LCA integration and offers a scalable and adaptable framework for sustainable construction, thereby facilitating real-time environmental assessments and supporting certification requirements with greater precision.