A Conceptual Framework for an AI-Enabled Digital Twin Platform LCA for Megaconstruction Projects

Megaconstruction projects, including high-speed rail networks, airports, seaports, and large urban infrastructure programmes, rank among the most resource-intensive activities in the built environment, generating substantial carbon emissions and persistent environmental degradation. These projects face compounding operational challenges: extended construction periods, supply chains that cross multiple continents, and governance structures that involve dozens of contracting organisations, producing data volumes that conventional static Life Cycle Assessment (LCA) cannot accommodate. LCA offers a robust framework for evaluating environmental performance across infrastructure lifecycles, but traditional approaches remain static and retrospective, which limits their use in dynamic, large-scale construction environments. This paper proposes a conceptual framework that integrates predictive machine learning and Digital Twin (DT) technologies within an AI-enabled digital platform, turning LCA into a continuous, real-time decision support process for megaconstruction projects. The framework was developed through a systematic literature review across the LCA, Digital Twin, and machine learning domains, and a structured four-layer design process covering data ingestion, AI predictive modelling, DT synchronisation, and interactive reporting, translated into a six-panel governance dashboard for project managers. Its completeness, coherence, and practical feasibility were then assessed through an Expert Validation Questionnaire. The framework is presented as a pre-empirical, expert-validated design; live deployment and performance testing on an operating megaconstruction project form the next stage of this research.