炭酸化制約下における低炭素コンクリートのAI支援逆設計
Artificial-intelligence-assisted inverse design of low-carbon concrete under carbonation constraints (原題)
Lenganji Simwanda, Paul Oluwaseun Awoyera, Adewumi John Babafemi
🤖 gxceed AI 要約
日本語
混合セメント系材料を用いたコンクリートのAI支援逆設計ワークフローを開発した研究。4つの公開データセットで学習したニューラルネットワークに、分割共形予測・連続処理フォレスト・多目的探索を組み合わせ、強度・内包炭素・炭酸化深さの同時最適化を試みた。72候補を生成し、物理的に妥当な候補は強度75.8MPa・内包炭素141.7・炭酸化深さ0.064mmと予測されたが、適用領域分析から実験検証が必要な仮説と位置づけている。
English
An AI-assisted inverse-design workflow for supplementary-cementitious-material concrete, combining Optuna-tuned neural networks, split conformal prediction, continuous-treatment forests, and multi-objective search across strength, embodied carbon, and carbonation depth. Of 72 generated candidates, a physically non-negative balanced mix predicted 75.8 MPa strength, 141.7 embodied carbon, and 0.064 mm carbonation depth. Applicability-domain analysis shows candidates fall outside strict joint confidence domains, framing them as lab-validation hypotheses rather than design-ready prescriptions.
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
建設業のScope 3・ embodied carbon 削減はSSBJ開示でも重要性が高い領域。AIによる材料設計の試みは、日本企業の低炭素建材調達・開示データ整備の参考になるが、実用化には実験検証が前提。
In the global GX context
Embodied carbon in construction materials is a growing focus under CSRD/ISSB Scope 3 disclosure and green-building procurement. This work illustrates both the promise and the pitfalls (leakage, applicability domain) of ML-driven low-carbon material design for disclosure-grade data.
👥 読者別の含意
🔬研究者:AIサロゲートモデルと多目的最適化を材料設計に適用する際の、データ漏洩監査や適用領域評価の手法が参考になる。
🏢実務担当者:低炭素コンクリート調達の候補探索にAIが使える可能性を示すが、現段階では実験検証が必須で即時の調達判断には使えない。
🏛政策担当者:建設分野の内包炭素削減に向けたAI活用の限界と検証要件を理解する材料になる。
📄 Abstract（原文）
This study develops and tests an artificial-intelligence-assisted inverse-design workflow for supplementary-cementitious-material-blended concrete, with artificial intelligence applied to surrogate prediction, uncertainty quantification, dosage-effect interpretation, and multi-objective candidate generation. Optuna-tuned feedforward neural networks were trained on four open datasets and coupled with split conformal prediction, continuous-treatment forest analysis, and non-dominated multi-objective search. The source-aware compressive-strength model achieved a mean absolute error of 7.20 megapascals and a coefficient of determination of 0.687. Under standard cross-validation, embodied carbon achieved a mean absolute error of 2.41 and a coefficient of determination of 0.999, while carbonation depth achieved a mean absolute error of 2.56 millimetres and a coefficient of determination of 0.847. A leakage audit showed that the exceptional embodied-carbon fit is largely attributable to an almost deterministic relation between mixture quantities and the carbon target, although performance was sensitive to source grouping. Continuous-treatment re-analysis showed substantial pre-adjustment imbalance and wide ninety-five-percent effect intervals, so the dosage results are interpreted conditionally rather than as universal causal effects. Three-objective optimization produced seventy-two computational candidates; sixteen, or 22.2 percent, had negative raw carbonation predictions and were excluded from representative reporting. A physically non-negative balanced candidate predicted 75.8 megapascals strength, 141.7 embodied carbon, and 0.064 millimetres carbonation depth. Applicability-domain analysis showed that none of the optimized candidates lay inside a strict joint ninety-five-percent domain for all retained endpoints, emphasizing that the generated mixtures are hypotheses for laboratory validation rather than design-ready prescriptions.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.engappai.2026.116633first seen 2026-11-07 04:53:05
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