Large Language Models Based In-Context Learning for Early Stage Building Life Cycle Assessment

This research investigates applying Large Language Models (LLMs) using in-context learning for early stage building Life Cycle Assessment (LCA). Building LCA is challenging at the early stage since information is limited and few decisions have been made. Traditionally, building LCA follows a linear workflow in which architects transfer designs to engineers for calculation, resulting in a time-consuming, ineffective process due to the knowledge gap between disciplines. Our study presents three LLM methods: innate, domain, and ontology-based knowledge approaches. We demonstrate that the ontology-based approach, utilizing an iterative process, reduces the Root Mean Square Error (RMSE) by 41% and standard deviation by 76% compared to the innate knowledge method. Our findings highlight the potential of LLMs as AI agents to integrate expert knowledge in early design by systematically providing domain knowledge. In-context learning represents a novel approach to addressing limitations in the early building LCA, with potential applications across engineering fields.