Ontology-driven BIM–LCA integration for assessing GHG emissions in metro station construction

Abstract

Purpose This study proposes an assessment framework of greenhouse gas emissions in metro station construction, integrating building information modeling (BIM) and process-based life cycle assessment (LCA). An automated tool is developed following this framework for decision makers to streamline the design of low-carbon metro stations. Design/methodology/approach The proposed ontology-driven framework maps construction processes to BIM elements while integrating environmental impact factors, enabling automated multi-level assessment via rule-based reasoning. It reconciles conflicts between element-based BIM techniques and process-based LCA methods. A cut-and-cover metro station case study has validated the efficacy of the framework and the efficiency of the automated tool developed. Findings The ontology-driven framework successfully resolves BIM–LCA granularity inconsistencies through an element-process-resource model, enabling semantically enriched data integration across engineering, environmental science and data analytics domains. The automated tool provides visual carbon profiling capabilities, supporting data-driven decision-making for low-carbon construction strategies. Originality/value The framework advances sustainable metro construction by providing a robust, automated methodology for environmental performance optimization in the design phase. The validated automated tool facilitates practical industry applications, transforming BIM data into actionable environmental insights during the design phase.