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<p>This study addresses the construction sector’s growing need for improved decision-making 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 CO<sub>2</sub> 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 <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="42162_2025_518_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="22" /> </InlineMediaObject> <EquationSource Format="TEX">\(m^2\)</EquationSource> </InlineEquation> and a 6% reduction in annual CO<sub>2</sub> 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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月1日

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  • Karim El Mounla,
  • Djaoued Beladjine,
  • Karim Beddiar

摘要

This study addresses the construction sector’s growing need for improved decision-making 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 \(m^2\) 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.