Optimizing Self-Compacting Concrete (SCC) Mix for Different Grades Using AI (ANN/RF models) for Low- Carbon Construction
異なる強度等級の自己充填コンクリート（SCC）配合をAI（ANN/RFモデル）で最適化する低炭素施工 (AI 翻訳)
Deepti Singh, Rakesh Kumar, Pooja
🤖 gxceed AI 要約
日本語
本研究は、自己充填コンクリート（SCC）の配合設計に人工知能（ANN、RF、GA）を適用し、強度と施工性を維持しながら炭素排出量を21～29%削減する最適配合を特定した。120サンプルのデータセットを用いてANNとRFモデルが強度・流動性・粘度・炭素排出量を予測し、RFが高精度を示した。遺伝的アルゴリズムにより低炭素かつ性能を満たす配合を自動探索する手法を確立した。
English
This study applies AI (ANN, RF, and GA) to optimize self-compacting concrete (SCC) mix designs, achieving 21–29% carbon emission reductions across grades M20–M60 while maintaining strength and workability. Using a dataset of 120 mixes, ANN and RF models predict compressive strength, slump flow, viscosity, and emissions; RF outperforms ANN. A genetic algorithm then identifies optimal proportions minimizing embodied carbon. The framework offers a reliable tool for sustainable concrete production.
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
本論文はAIを用いたコンクリート配合最適化によりCO2排出削減を実証しており、日本の建設業界における低炭素化ニーズ（例：国土交通省のグリーン建材調達ガイドライン）に直接的に関わる。JIS規格に基づくSCC配合設計への応用が期待される。
In the global GX context
This paper demonstrates an AI-driven framework for reducing concrete's embodied carbon, aligning with global efforts like the Global Cement and Concrete Association's Net Zero Roadmap. The methodology can be adapted to local materials and standards, offering a scalable approach for the construction sector's decarbonization.
👥 読者別の含意
🔬研究者:Provides a validated AI methodology (ANN/RF/GA) for multi-objective mix optimization that can be extended to other cementitious materials or performance criteria.
🏢実務担当者:Offers a practical tool to design low-carbon SCC mixes that meet strength and workability requirements, potentially reducing material costs and carbon footprint.
🏛政策担当者:Supports the feasibility of AI-driven design for achieving carbon reduction targets in construction without compromising performance.
📄 Abstract（原文）
The construction industry is under increasing pressure to reduce carbon emissions while maintaining the performance requirements of modern infrastructure. Self-Compacting Concrete (SCC) has gained widespread acceptance due to its superior workability, self-compaction ability, and enhanced durability; however, its high cementitious material content often results in a significant carbon footprint. This study presents an Artificial Intelligence (AI)-based framework for optimizing SCC mix designs across different concrete grades (M20, M30, M40, M50, and M60) to achieve low-carbon and sustainable construction. A dataset comprising 120 SCC mix samples with varying proportions of Ordinary Portland Cement (OPC), Ground Granulated Blast Furnace Slag (GGBS), Fly Ash, Limestone Calcined Clay Cement (LC3), and water-binder ratios was developed and utilized for model training and validation. Artificial Neural Network (ANN) and Random Forest (RF) models were employed to predict key SCC properties, including compressive strength, slump flow, viscosity, and carbon emissions. The predictive models demonstrated high accuracy, with RF outperforming ANN in terms of prediction performance. Subsequently, a Genetic Algorithm (GA) was integrated to identify optimal mix proportions that minimize embodied carbon while satisfying strength and workability requirements. The optimized SCC mixtures achieved carbon emission reductions ranging from 21% to 29% across different concrete grades without compromising mechanical performance or SCC workability criteria. The results demonstrate that the proposed AI-driven optimization framework provides an effective and reliable approach for designing sustainable SCC mixes and offers significant potential for advancing low-carbon construction practices in the concrete industry.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.55544/jrasb.icaces.9first seen 2026-06-29 05:21:38 · last seen 2026-06-29 05:21:45
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