BIM-Assisted Artificial Intelligence Carbon Modeling for Green and Low Carbon Building Designs
グリーンおよび低炭素建築設計のためのBIM支援人工知能炭素モデリング (AI 翻訳)
Fangning Sun
🤖 gxceed AI 要約
日本語
本研究では、BIM技術を用いて建物の在庫データと排出データを収集し、相関分析とElastic Netアルゴリズムで炭素排出に影響する設計特徴を選定。改良グレイウルフ最適化に基づくSVMモデル（IGWO-SVM）で排出予測モデルを構築し、R²=0.811を達成。他のモデルより9.45%～125.91%優れており、建築家の炭素排出推定精度向上に寄与する。
English
This study uses BIM to collect inventory and emission data from buildings, then applies correlation analysis and elastic net to select 8 design features affecting carbon emissions. An improved grey wolf optimization-based SVM (IGWO-SVM) model is developed, achieving R²=0.811, outperforming other models by 9.45% to 125.91%. It helps architects estimate carbon emissions accurately for low-carbon building designs.
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
日本でも建築分野の脱炭素が進む中、BIMとAIを活用した炭素排出予測は、設計段階での排出量可視化に有効。ただし、日本の建築基準や排出係数との整合性は別途検討が必要。
In the global GX context
Global building sector emissions require accurate prediction tools. This BIM-AI approach offers a data-driven method for early-stage carbon estimation, relevant for green building certifications and net-zero targets. The IGWO-SVM model shows improved accuracy over standard methods.
👥 読者別の含意
🔬研究者:Provides a novel hybrid model (IGWO-SVM) for building carbon prediction, with performance benchmarks against other ML models.
🏢実務担当者:Architects and engineers can use this tool for low-carbon design optimization and carbon footprint estimation in BIM environments.
🏛政策担当者:Supports development of building carbon emission standards and green building codes by enabling accurate predictive modeling.
📄 Abstract（原文）
Nowadays, climate change has become a serious issue for the world, and the creation of low-carbon green buildings becomes one of the ways offered by humans to overcome the existing problems. In this research, through BIM technology, inventory data and emission data from selected buildings have been collected. With correlation analysis and elastic net algorithm, design features affecting building carbon emissions have been screened out, and 8 features were regarded as predictors. Then, an improved gray wolf optimization algorithm-based support vector machine method (IGWO-SVM) is utilized to establish the prediction model of building carbon emissions. Through model comparisons, it has been found that our IGWO-SVM model has attained an R² value of 0.811, which is 9.45% to 125.91% better than other models, while the metrics of RMSE, MAE, NRMSE, and CV(RMSE) have reached the lowest values compared to other models with at least 12.26% lower performance. It will help architects estimate carbon emissions accurately, enabling green and low-carbon buildings to be promoted.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.65102/is2026128first seen 2026-05-17 05:23:36
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。