AUTOMATED QUANTIFICATION AND PREDICTIVE ANALYSIS OF EMBODIED CARBON FOR AS-DESIGNED AND AS-BUILT BIM MODELS TOWARDS ACHIEVING CLIMATE RESILIENCE

Abstract

Reducing embodied carbon (EC) emissions is becoming more and more important as the construction industry faces the necessity of addressing climate change. This study conducts a thorough investigation into the automated quantification and predictive analysis of EC in as-designed and as-built Building Information Modelling (BIM) models, with a specific focus on NUS’s 5G Centre. The study uses advanced BIM technology to measure EC in both as-designed and as-built BIM models, allowing for a comparative examination of their respective carbon footprints. Through this quantification process, this study analyses the impact of various construction materials on EC emissions. Furthermore, predictive analysis techniques were used to forecast the potential environmental implications of material replacements in BIM models, providing useful information for future construction projects. This study exhibits strong compliance with the Life Cycle Assessment (LCA) methodology, allowing for extensive comparisons of EC footprints across several categories of BIM models, such as materials, components, and stages of the LCA. This paper serves to advance climate resilience in construction practices by providing stakeholders with practical insights to reduce environmental impacts and enhance sustainability in the built environment.