不確実性を考慮した目標曲線誘導型多目的逆設計フレームワーク：軸圧縮下の低炭素方形石炭ガングコンクリート充填鋼管短柱
Uncertainty-aware target-curve guided multi-objective inverse design framework for low-carbon square coal gangue concrete-filled steel tube stub columns under axial compression (原題)
Yuqing Zhao, Xiangyu Kong, Jinlong Liu, Chungang Wang, Yafeng Wang, Yuzhuo Zhang
🤖 gxceed AI 要約
日本語
石炭ガング（炭鉱廃棄物）を骨材代替した低炭素CFST柱の設計手法を提案。1021本の応力-ひずみ曲線データベースで9種のMLサロゲートを学習し、LSTMがR²=0.9996を達成。曲線誤差・天然骨材量・鋼比の3目的逆設計をモンテカルロ不確実性下で解き、NSGA-IIIとMODEが良好なパレート解を生成。実務用ソフトも開発。
English
Proposes a target-curve-guided multi-objective inverse design (UTC-MOID) framework for low-carbon square coal gangue CFST stub columns. Nine ML surrogates trained on 1021 stress-strain curves; LSTM achieves R²=0.9996. Optimizes curve error, natural aggregate use, and steel ratio under Monte Carlo uncertainty; NSGA-III and MODE yield superior Pareto fronts. Software supports practical design selection.
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
建設分野のScope 3（原材料調達）削減や低炭素建材の調達判断に関わる。炭鉱廃棄物の有効利用は資源循環・カーボンニュートラル政策と親和的だが、日本の開示制度（SSBJ等）との直接接続は薄い。
In the global GX context
Addresses embodied-carbon reduction in construction materials via waste valorization, relevant to Scope 3 category 1 (purchased goods) accounting and green procurement. However, it sits outside mainstream disclosure frameworks (TCFD/ISSB/CSRD) and offers limited direct linkage to climate disclosure scholarship.
👥 読者別の含意
🔬研究者:MLサロゲートと多目的進化計算を組み合わせた材料逆設計の手法論として参考になる。
🏢実務担当者:低炭素建材の性能等価代替を検討する建設・調達担当者に、設計トレードオフの定量化手法を提供。
🏛政策担当者:建設資材の炭素削減を促す調達基準や廃棄物利用政策の技術的裏付けになり得る。
📄 Abstract（原文）
Square coal gangue concrete-filled steel tube columns (CGCFST) offer a promising route for the low-carbon substitution of conventional concrete-filled steel tube (CFST) members, but existing design methods are mainly capacity-oriented and cannot reproduce the full stress-strain response required for performance-equivalent replacement. This study proposes a target-curve-guided multi-objective inverse design (UTC-MOID) framework for square CGCFST stub columns under parameter uncertainty and design-code constraints. A database containing 1021 valid axial-compression stress-strain curves and 53131 sampling points was established via finite element analysis and used to train nine machine-learning surrogate models. The long short-term memory model achieved the best full-curve prediction accuracy, with a test R-squared ( R 2 ) of 0.9996. Based on this, the inverse design problem was formulated with three objectives: minimizing the expected full-curve error for mechanical performance requirements, reducing natural aggregate content for sustainability, and reducing the steel ratio for cost efficiency. Monte Carlo simulation was incorporated to account for uncertainties in surrogate-model prediction error, geometry, and material strengths. Four evolutionary algorithms were compared, and representative Pareto solutions were selected for different design preferences. Results show that Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Multi-objective Differential Evolution (MODE) provide better Pareto-front quality, while steel yield strength and concrete strength dominate the UTC-MOID designs. External CFST curve validation demonstrates that the proposed framework can generate CGCFST alternatives with high curve fidelity, including an R 2 of 0.9965 for the accuracy-prioritized solution. The developed UTC-MOID software further supports practical target-curve import, constraint checking, Pareto visualization, and design selection.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.advengsoft.2026.104322first seen 2026-10-01 04:54:33
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