Integrating BIM with Lean Principles for Enhanced Decision-making: Optimizing Insulation Material Selection in Sustainable Construction Project

This study addresses the construction sector’s growing need for improved decisionmaking
and reduced carbon emissions by integrating Lean principles into Building
Information Modeling (BIM). A decision-support tool was developed using Python
and RStudio to enhance stakeholder efficiency, reduce errors, and streamline
communication. The tool combines Set-Based Design, Choosing By Advantages, and
Big Room methods with Industry Foundation Classes (IFC) data to automatically
generate and evaluate insulation options based on multi-criteria analysis. To test its
adaptability and effectiveness, the tool was applied to two real-world case studies in
different regions of France with distinct climatic conditions and project objectives.
The first case study involved a mixed-use building in Rennes, where the objective
was to enhance energy performance. The selected insulation material reduced
heating needs by 13%, annual CO2 emissions by 14%, and insulation costs by 45%
over a 50-year period. The second case study focused on a residential building in
Orléans, where the goal was to improve both energy efficiency and environmental
impact. The tool achieved a 6% reduction in primary energy consumption, a 40%
decrease in carbon footprint per m2 and a 6% reduction in annual CO2 emissions.
The tool’s ability to adapt to different building types and climatic conditions
confirms its accuracy and reliability in optimizing energy performance and reducing
environmental impact and project costs. This research provides a scalable tool for
enhancing decision-making efficiency and improving building energy performance,
environmental impact, and cost-effectiveness in construction projects.