What question did this study set out to answer?

The primary aim is to reduce embodied greenhouse gas emissions during the construction design phase using BIM and LCA methodologies.

July 5, 2026Open Access

Research on Low Carbon During the Construction Design Process Based on BIM and Life Cycle Assessment

Key Points

The primary aim is to reduce embodied greenhouse gas emissions during the construction design phase using BIM and LCA methodologies.
Developed an uncertainty-aware BIM–LCA methodology with automated quantity takeoff and probabilistic carbon assessment.
Utilized Monte Carlo simulation to assess uncertainty in material quantities and carbon emissions.
Created a machine-learning surrogate model from 1200 design samples for optimization.
Achieved a 38.13% reduction in embodied carbon, decreasing GWP to 318.21 kgCO2e/m2.
Identified product-stage emissions from materials (A1–A3) as 89.28% of total embodied carbon.
Enhancing BIM maturity lowered uncertainty by roughly 20%.

Abstract

Reducing embodied greenhouse gas emissions in the initial design phase is essential for attaining low-carbon buildings, as the highest potential for reduction exists prior to the finalization of construction decisions. While Building Information Modelling (BIM) and Life Cycle Assessment (LCA) have been progressively integrated for embodied carbon evaluation, current frameworks are predominantly deterministic, offer minimal uncertainty quantification, and seldom utilize machine-learning-assisted optimization to facilitate design decision-making. This paper presents an uncertainty-aware BIM–LCA methodology to solve these shortcomings, integrating automated quantity takeoff, probabilistic carbon assessment, and explainable machine-learning optimization. The proposed methodology integrates IFC-based BIM models, Bills of Quantities (BoQs), and regional life cycle inventory databases to conduct a cradle-to-grave embodied carbon assessment. Quantities produced from BIM were checked against BoQ data, and the uncertainty related to material quantities and emission factors was assessed by Monte Carlo simulation. A machine-learning surrogate model was created with 1200 design samples to facilitate swift optimization, and SHapley Additive exPlanations (SHAPs) were utilized to determine the most significant design factors. A mid-rise residential structure in Chongqing, China, encompassing a gross floor area of 9750.03 m2, was used as a case study. The baseline Global Warming Potential (GWP) was calculated as 514.29 ± 30.09 kgCO2e/m2 (A1–A5), with product-stage emissions (A1–A3) accounting for roughly 89.28% of total embodied carbon, predominantly from concrete and steel. Enhanced BIM maturity lowered uncertainty by roughly 20%. Optimization resulted in a 38.13% decrease in embodied carbon, reducing GWP to 318.21 kgCO2e/m2. SHAP research identified the percentage of material reuse and concrete composition as the primary factors influencing carbon reduction. The suggested framework offers a clear and replicable decision-support mechanism for low-carbon building design that accounts for uncertainty.

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