AI-Supported Process Optimization Framework for Building Life Cycle Assessment

Life cycle assessment (LCA) as an environmental management tool is a crucial systematic that analyzes the environmental impacts of the construction industry in a systematic and integrated manner. Artificial intelligence (AI) is increasingly being used, particularly to facilitate data integration between the construction industry and environmental performance. However, AI-supported LCA studies in the literature prodominantly focus on estimating environmental impact indicators. Comprehensive and powerful software such as GaBi is available for environmental impact assessment. In this respect, there is a need for an approach that will support and optimize LCA processes. This study focuses on AI-supported process optimization and proposes an AI-supported LCA methodology. GaBi thinkstep software was used for LCA. With the process optimization approach, the aim is to transform the environmental data obtained in GaBi into normalized environmental performance indicators at the building scale with the support of AI. A workflow (coding) to generate normalized values was defined using Python software. Alternative structural systems were analyzed in line with the proposed methodology. Studies on process optimization are limited in the literature, and this study aims to contribute to the literature.