低炭素超高性能コンクリート：原材料、粒子充填最適化、データ駆動型性能評価に関するレビュー
Low-carbon ultra-high-performance concrete: raw materials, particle packing optimization, and data-driven performance assessment—a review (原題)
Ahmed Fageeri, Ousmane Hisseine
🤖 gxceed AI 要約
日本語
本研究は200以上の配合を文献調査とデータ駆動型分析で統合し、UHPCの配合設計・性能・埋め込みCO2排出量の定量関係を解明した。ランダムフォレストは圧縮強度予測で高精度を示し、セメント量800kg/m3超は機械的特性への寄与が小さくCO2を大幅に増やすことを明らかにした。低炭素UHPC配合設計への実用的知見を提供する。
English
This review couples a literature survey with data-driven analysis of 200+ UHPC mixtures to link mix design, mechanical/durability performance, and embodied CO2. Random Forest predicts compressive strength accurately, and cement content above 800 kg/m3 adds negligible strength while sharply raising embodied carbon. It offers practical guidance for low-carbon UHPC formulation.
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排出削減、特にコンクリート由来の埋め込み炭素管理は、SSBJ・有報でのサプライチェーン排出開示と直結する。日本企業が低炭素建材の調達・設計を進める上での定量的根拠を提供する。
In the global GX context
Embodied carbon in concrete is a major Scope 3 category for construction and real estate, directly relevant to CSRD/ISSB disclosure and green building certification. The data-driven mix-design framework supports global efforts to decarbonize cement-intensive materials.
👥 読者別の含意
🔬研究者:UHPCの配合設計と埋め込み炭素の定量関係、および機械学習による性能予測の限界を示す。
🏢実務担当者:セメント量最適化により強度を維持しつつ埋め込みCO2を削減する配合設計の指針を得られる。
🏛政策担当者:建設資材の低炭素化に向けた規制・調達基準の設計に、性能と炭素のトレードオフの定量的根拠を提供する。
📄 Abstract（原文）
Ultra-high-performance concrete (UHPC) offers exceptional mechanical properties and durability; however, its cement-intensive formulations raise concerns regarding environmental sustainability. This study addresses this challenge by establishing quantitative relationships between UHPC mixture design, performance indicators, and embodied CO 2 emissions by coupling a literature survey with data-driven analysis of over 200 mixtures. Unlike previous studies that examine the behavior of raw materials and the resulting performance in isolation, this study leverages data-driven approaches to link mixture design parameters to mechanical performance, durability indicators, and embodied CO 2 emissions, thereby providing useful insights into low-carbon UHPC formulations. The influence of supplementary cementitious materials and mineral fillers is evaluated across rheological, mechanical, durability, and carbon footprint aspects. Machine learning (i.e., Random Forest) analysis demonstrates high accuracy in predicting compressive strength from mixture design parameters, while tensile properties and permeability remain influenced by microstructural factors not captured by mixture design parameters. Heat map correlation reveals strong interdependencies among mechanical and durability performance indicators, with compressive strength highly correlated (95%–98%) across curing ages and inversely related to permeability-related durability metrics. An exponential relationship between cement-to-binder and water-to-cement ratios is established ( R 2 of 95%), providing a strong predictive tool for mixture design. From a carbon footprint perspective, cement is the dominant contributor to CO 2 emissions, as expected. Interestingly, cement content above 800 kg/m 3 has negligible contributions to mechanical properties while significantly increasing the embodied CO 2 emissions. By adopting a data-driven approach, this study provides novel insights into designing UHPC mixtures that foster higher mechanical and durability performance while reducing embodied carbon.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s44242-026-00116-xfirst seen 2026-09-23 05:00:26
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