Research on Low Carbon During the Construction Design Process Based on BIM and Life Cycle Assessment
BIMとライフサイクルアセスメントに基づく建設設計プロセスにおける低炭素化の研究 (AI 翻訳)
Basaula Pululu Jordan, Xinyu Yang, Y Shi, Shanzhi Wang, Xuan Cao, Daren Zhang, Yi Yang, Hao Peng
🤖 gxceed AI 要約
日本語
建築設計段階での低炭素化に向けて、BIMとLCAを統合し、不確実性を考慮した機械学習最適化手法を提案。中国重慶の中層住宅を事例に、A1-A5のGWPを38.13%削減。SHAP分析により材料再利用割合とコンクリート組成が重要因子と特定。再現可能な意思決定支援フレームワークを提供。
English
This paper proposes an uncertainty-aware BIM-LCA methodology integrating automated quantity takeoff, probabilistic carbon assessment, and explainable machine learning optimization to reduce embodied carbon in building design. Using a case study in Chongqing, China, they achieved a 38.13% reduction in global warming potential (cradle-to-grave). SHAP analysis identified material reuse and concrete composition as key design factors. The framework is replicable and supports decision-making under uncertainty.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の建築業界では、国土交通省の建築物省エネ法や建設業のグリーン化が進む中、BIMとLCAの統合は重要。特にSSBJや有報でのGHG排出量開示が建築セクターにも影響する。本手法は不確実性を定量化し、設計段階での最適化を可能にする点で、日本の建設会社や設計事務所が低炭素設計を推進する際の参考になる。
In the global GX context
Globally, building decarbonization is critical for meeting Paris Agreement targets. This study integrates machine learning with LCA to optimize embodied carbon in BIM environments, aligning with TCFD/ISSB emphasis on scenario analysis and uncertainty quantification. The framework is applicable to ISSB-compliant reporting and sustainable construction practices.
👥 読者別の含意
🔬研究者:BIM-LCAと機械学習最適化の統合手法、特に不確実性定量化とSHAP解釈の応用に興味のある研究者にとって有益。
🏢実務担当者:設計段階でカーボン削減を可視化し、材料選定に活用できるため、建設会社・設計事務所の実務に直接役立つ。
🏛政策担当者:建築の低炭素化推進のための規制や補助金の根拠データとして、またサプライチェーンでのGHG算定ルール策定に参考となる。
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/buildings16132653first seen 2026-07-05 05:01:43
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