INTEGRATING ARTIFICIAL INTELLIGENCE INTO LIFE CYCLE ASSESSMENT IN THE BUILDING INDUSTRY: A BIBLIOMETRIC AND CRITICAL REVIEW

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Identifies key gaps in AI-LCA literature, guiding future research on data standardization and real-time monitoring in construction LCA.

🏢実務担当者:Highlights how AI can improve LCA accuracy and efficiency, but warns of current data fragmentation challenges.

🏛政策担当者:Suggests the need for standardized data schemas and interoperability to enable AI-driven LCA for policy compliance.

Increasing environmental impacts of buildings necessitate robust sustainability assessment tools, positioning Life Cycle Assessment (LCA) as a central methodology for evaluating environmental performance. Artificial Intelligence (AI) offers strategic potential to enhance the accuracy, efficiency, and automation of LCA processes. This study critically reviews AI-integrated LCA research in the construction sector. A bibliometric analysis of 883 publications from Web of Science and Scopus was conducted, alongside a systematic review of 18 articles explicitly integrating AI into LCA workflows. Findings show Machine Learning (ML) and Artificial Neural Networks (ANN) are predominantly used to predict energy consumption and carbon emissions. However, AI-LCA integration remains fragmented due to unstructured data, lack of standardized protocols, low interoperability, and restricted access to high-quality datasets. Current literature primarily focuses on operational energy use, largely neglecting embodied impacts and broader sustainability indicators. Future research should prioritize AI frameworks incorporating standardized data schemas, real-time monitoring, and case-based validation. Integrating AI into LCA offers transformative potential for data-driven, transparent, and adaptable sustainability strategies in construction.

• semanticscholar https://doi.org/10.17482/uumfd.1649878first seen 2026-06-29 06:55:14