Model Prediction and Multi-Objective Optimization of Unfired Bricks Incorporated with Drinking Water Treatment Sludge Using Machine Learning
機械学習を用いた水道水処理汚泥混合無焼成レンガのモデル予測と多目的最適化 (AI 翻訳)
Xiaomeng Han, Shihao Wang, Zhen Zhou, Guang Chen, Hai-juan Wei, Yangyang Chu, Xiaotian Liu
🤖 gxceed AI 要約
日本語
水道水処理汚泥（DWTS）を無焼成レンガに混入する手法に対し、機械学習（ランダムフォレスト、XGBoost）とパレート最適化を適用。圧縮強度とコストの両立を実現し、最適サンプルで15.08 MPaの強度を達成。SHAP分析により養生方法やDWTS比率の影響を可視化。
English
This study uses machine learning (RF, XGBoost) and Pareto optimization to optimize unfired bricks containing drinking water treatment sludge. The optimal sample achieved 15.08 MPa compressive strength with negative cost (-2.4 RMB). SHAP analysis identified key factors: early water immersion and DWTS proportion.
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
日本では建設廃材や下水汚泥のリサイクルが進むが、水道汚泥の利活用は限定的。本手法は日本の廃棄物処理・建設分野での低炭素素材開発に示唆を与える。
In the global GX context
This work contributes to circular economy and low-carbon construction materials globally, though its direct relevance to climate disclosure frameworks (TCFD/ISSB) is limited. The ML optimization approach could inspire similar waste valorization studies.
👥 読者別の含意
🔬研究者:MLを用いた材料最適化の実証例として有効。
🏢実務担当者:廃棄物処理業者や建材メーカーが低コスト・低炭素材開発に活用可能。
📄 Abstract（原文）
Incorporating drinking water treatment sludge (DWTS) into unfired bricks provides a promising approach for large-scale utilization with low carbon emission. However, the complex effects of material composition and curing strategy on unfired brick performance are difficult to optimize through conventional trial-and-error methods. Therefore, in this study machine learning (ML) combined with Pareto front analysis was introduced to develop a multi-objective optimization of both compressive strength and cost. Among the tested models, the random forest (RF) and extreme gradient boosting (XGBoost) exhibited the best generalization performance for predicting compressive strength and cost based on the 5-fold cross validation, respectively. SHapley Additive exPlanation (SHAP) analysis revealed that early water immersion followed by standard curing strongly enhanced compressive strength and DWTS proportion had the greatest negative influence on cost. Pareto optimization identified a trade-off scheme with the predicted compressive strength of 15.5 MPa and a negative cost of −2.4 RMB. The measured compressive strength of this optimal sample was 15.08 MPa, close to the predicted value and much higher than that of the reference sample. Scanning electron microscopy (SEM) and thermogravimetry analysis (TGA) results further confirmed abundant hydration products in the optimal sample. This study highlights the potential of ML to guide DWTS utilization in unfired bricks while balancing compressive strength and cost.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/buildings16122336first seen 2026-06-16 05:04:08
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