Machine Learning–Driven Optimization of Low-Carbon Concrete Mixes Incorporating Industrial Waste
産業廃棄物を活用した低炭素コンクリート配合の機械学習駆動最適化 (AI 翻訳)
Abdullah Naser M. Asiri
🤖 gxceed AI 要約
日本語
本研究は、産業廃棄物を活用した低炭素コンクリートの開発と機械学習による強度予測・配合最適化を統合したフレームワークを提案。フライアッシュと高炉スラグをセメント代替として用い、300データでRF、SVR、XGBoostを比較し、XGBoostが最高精度を示した。長期的強度を維持しつつCO2排出削減が可能であることを実証。
English
This study proposes a unified framework integrating industrial waste-based low-carbon concrete development with machine-learning-driven strength prediction and optimization. Using fly ash and GGBS, 300 experimental observations were modeled with RF, SVR, and XGBoost. XGBoost achieved highest accuracy, demonstrating that optimized low-carbon mixes can reduce CO2 while maintaining long-term strength.
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CO2排出削減が急務であり、セメント代替材と機械学習による効率的な配合設計は、2030年目標達成に資する。本研究成果は、日本のコンクリート製造現場でのトライアル削減やコスト低減に応用可能。
In the global GX context
Globally, concrete accounts for ~8% of CO2 emissions. This study's ML-driven optimization of low-carbon mixes using industrial waste offers a scalable pathway to reduce cement use. It aligns with construction sector decarbonization targets and provides a data-driven method to accelerate adoption of sustainable concrete worldwide.
👥 読者別の含意
🔬研究者:Demonstrates a successful integration of ML (XGBoost) with experimental concrete design, useful for researchers in sustainable materials and ML applications.
🏢実務担当者:Construction and materials firms can use the ML-based prediction to minimize trial batches, accelerating development of low-carbon concrete products.
🏛政策担当者:Supports policies promoting industrial waste utilization and low-carbon building materials by showing quantifiable strength and emissions benefits.
📄 Abstract（原文）
The cement industry is a major contributor to global carbon dioxide (CO₂) emissions, necessitating the development of sustainable construction materials with reduced environmental impact. This study proposes a novel integrated framework that combines industrial waste-based low-carbon concrete development with machine-learning-driven strength prediction and mix optimization. Low-carbon concrete mixes were produced by partially replacing ordinary Portland cement with fly ash and ground granulated blast furnace slag (GGBS) at various replacement levels. Experimental investigations were conducted to evaluate compressive strength development at different curing ages. A dataset comprising 300 experimental observations was subsequently employed to develop and compare three machine learning models, namely Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost), for compressive strength prediction. The results demonstrate that appropriately designed low-carbon concrete mixtures can achieve comparable or superior long-term strength while substantially reducing cement consumption and associated CO₂ emissions. Among the evaluated models, XGBoost exhibited the highest predictive accuracy, indicating its suitability for sustainable concrete mix optimization. The novelty of this study lies in integrating experimental low-carbon concrete design, environmental assessment, and advanced machine learning techniques within a unified framework to enhance structural performance and sustainability simultaneously. The proposed approach provides an efficient pathway to reduce trial-and-error experimentation and accelerate the adoption of eco-friendly concrete in modern construction practices.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.54740/ros.2026.030first seen 2026-07-10 05:15:54 · last seen 2026-07-10 05:28:56
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