解釈可能な機械学習によるセラミック廃棄物コンクリートの持続性と感度進化分析
Interpretable machine learning-based sustainability and sensitivity evolution analysis of ceramic waste concrete (原題)
B. T, Sabarigiri Selvaraj, Tamil Priyan R. K. B, S. S, P. P, Dhivakar S
🤖 gxceed AI 要約
日本語
建設廃棄物であるセラミックタイルと粉末を粗骨材（0-100%）およびセメント（0-35%）の代替として用いたコンクリートの持続性を評価した。28日圧縮強度はセメント35%代替で約2.5%の低下にとどまる一方、CO₂排出量は約32%削減され、持続性・性能指数（SPI）は最大44%向上した。SHAPを用いた解釈可能な機械学習により、セメント関連パラメータが強度に強く影響することを示し、許容可能な機械的性能を維持する廃棄物利用範囲を特定した。
English
This study evaluates sustainable concrete using ceramic waste and powder to partially replace coarse aggregates (0-100%) and cement (0-35%). At 35% cement replacement, 28-day compressive strength dropped only ~2.5% while embodied CO2 emissions fell ~32%, improving the Sustainability-Performance Index (SPI) by up to 44%. Interpretable ML with SHAP identified cement-related parameters as dominant for strength, and scenario-based predictions defined feasible waste utilization ranges.
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
日本では建設業の脱炭素化と廃棄物削減が重要課題であり、本論文はセラミック廃棄物を活用した低炭素コンクリートの実現可能性を示す。SSBJや有報でのScope 3排出量開示や、建設業のGX推進に資する定量的根拠を提供する。
In the global GX context
Globally, this paper contributes to the growing body of research on low-carbon construction materials, aligning with TCFD/ISSB disclosure of Scope 3 emissions and circular economy goals. It offers a data-driven framework for integrating environmental impact with mechanical performance, useful for CSRD reporting and green building standards.
👥 読者別の含意
🔬研究者:機械学習と持続性指標を組み合わせた材料評価手法は、他の廃棄物活用研究にも応用可能。
🏢実務担当者:セラミック廃棄物の利用範囲と炭素削減効果を定量的に示し、調達・設計判断に活用できる。
🏛政策担当者:建設廃棄物の再利用促進と低炭素建材の普及に向けた規制・インセンティブ設計の参考になる。
📄 Abstract（原文）
The usage of natural resources has been largely increased due to construction activities and conventional concrete causes harmful effects to the environment. To reduce these impacts, shifting to sustainable construction materials in concrete technology is essential. Utilization of construction demolition wastes like ceramic tiles and ceramic powder is a better strategy to reduce solid waste footprint. In this study, waste ceramics and ceramic powder are used to partially replace coarse aggregates (0-100%) and cement (0-35%) respectively. Experimental evaluation of workability and mechanical properties was conducted for M20 grade concrete, followed by interpretable machine learning model to analyze the influence of different parameters. Sustainability–Performance Index (SPI) was calculated to integrate mechanical performance with environmental impacts, by quantifying the proposed concrete’s carbon emission. Results indicate that while 28-day compressive strength shows less reduction by approximately 2.5% at 35% cement replacement, embodied CO₂ emissions reduction was achieved by nearly 32%. This indicates a maximum SPI improvement of about 44% compared to conventional concrete. The comparison of different ML models and parameters analysis by SHapley Additive exPlanations (SHAP) showed that cement related parameters influence strength development greatly, whereas aggregate replacement showed comparatively minor influence. Additionally, feasible ranges of ceramic waste utilization that maintain acceptable mechanical performance were identified using scenario-based predictions. The findings demonstrate that substantial carbon reduction can be achieved without compromising structural requirements, providing a robust decision-support framework for resilient infrastructure.
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- semanticscholar https://jresm.org/wp-content/uploads/resm2026-1575ma0319rs.pdffirst seen 2026-10-02 05:36:14
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