Development of a BIM-based AI-driven matching tool for LCA datasets

Abstract

The construction sector significantly contributes to environmental issues and often relies on Life Cycle Assessment (LCA) for the quantification and optimization of its environmental impacts. One of the most time- and labour-intensive tasks in LCA is matching real elements (e.g., construction elements and materials) to suitable environmental datasets to get an idea of the element’s sustainability performance (emissions). In this regard, this study presents an open-access software tool that leverages artificial intelligence (AI) to support the matching process between construction elements in Building Information Modelling (BIM) with corresponding environmental datasets in a semi-automatic manner. Developed in Python and using the GPT-4o mini model from OpenAI for its matching mechanism, the tool demonstrates how AI-driven digital innovation can improve efficiency, reduce manual effort, and enhance early-stage environmental assessment in construction planning, while integrating sustainability data into BIM workflows. Through a series of use cases, the software’s ability to address key challenges in the integration of BIM and LCA tools is demonstrated, showcasing a high degree of automation and interoperability. Moreover, the accessible design of the tool allows use without extensive technical knowledge. The conducted validation tests confirmed the tool’s potential for accurate LCA matching, highlighting opportunities for AI to enhance sustainability workflows while offering BIM experts a better understanding of the challenges in sustainability assessment.