もみ殻灰の統合：持続可能なセメント系材料設計のための材料科学と人工知能
Integrating Rice Husk Ash- Material Science and Artificial Intelligence for Sustainable Cementitious Material Design (原題)
P. H. Bindu, Swati Agrawal, R. R. L. Birali
🤖 gxceed AI 要約
日本語
本レビューは、もみ殻灰（RHA）を用いた低炭素セメント系材料の設計において、材料科学とAI/MLを統合する枠組みを提案する。RHA性能を左右する多数の相互作用因子を整理し、特徴量設計・説明可能・不確実性対応・物理情報MLの重要性を論じる。RHA特性評価→予測モデリング→検証→耐久性・寿命→LCA多目的最適化という統合フローにより、試行錯誤から知的設計への移行を目指す。
English
This review integrates rice husk ash (RHA) material science with AI/ML to enable intelligent design of low-carbon cementitious materials. It maps the interacting factors governing RHA performance and highlights feature engineering, explainable, uncertainty-aware, and physics-informed ML. A proposed framework spans RHA characterization, predictive modeling, validation, durability/service-life, and LCA-based multi-objective optimization, shifting from trial-and-error to data-driven design.
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
日本の建設業はScope 3排出と資材調達の脱炭素が課題であり、RHA等の副産物活用はセメント由来CO2削減に寄与する。SSBJ開示やサプライチェーン排出管理を進める企業にとって、LCA連動型の材料設計手法は調達・開示両面で示唆がある。
In the global GX context
Globally, this supports the decarbonization of cement and concrete, a hard-to-abate sector central to CSRD/ISSB Scope 3 and embodied-carbon disclosure. The AI-driven, LCA-integrated design framework offers a template for linking material innovation to verifiable low-carbon claims in transition finance and green procurement.
👥 読者別の含意
🔬研究者:材料科学とAI/MLの統合、特に説明可能・物理情報MLとLCA多目的最適化の研究課題を提示する。
🏢実務担当者:セメント・建設企業はRHA活用とAI予測モデルにより、低炭素材料の調達・設計コストと排出削減を両立できる可能性がある。
🏛政策担当者:建設資材の脱炭素を促す規制・調達基準において、副産物利用とデータ駆動設計を支援する枠組みの参考になる。
📄 Abstract（原文）
The increasing demand for sustainable and low-carbon construction materials has intensified research into supplementary cementitious materials derived from agricultural residues. Rice husk ash (RHA), owing to its high amorphous silica content, pozzolanic activity, porous morphology, and potential to reduce Portland cement consumption, represents a promising alternative for developing sustainable cementitious composites. Nevertheless, the engineering performance of RHA-based mixtures is governed by multiple interacting factors, including ash composition and reactivity, burning conditions, particle size, replacement ratio, water-to-binder ratio, curing regime, mixture proportions, and environmental exposure. The nonlinear interactions among these variables make conventional experimental optimization costly, labor-intensive, and difficult to generalize. Demand for low-carbon materials has increased interest in Rice Husk Ash (RHA) as SCM due to its high amorphous silica, pozzolanic activity and cement-saving potential. However, RHA performance depends on many interacting factors - burning conditions, particle size, replacement %, w/b, curing and exposure - making trial-and-error optimization costly.AI/ML [ANN][SVR][RF][GPR] has been used mainly for compressive strength prediction, but durability, service-life and sustainability applications remain limited due to incomplete characterization, heterogeneous data, poor validation and lack of interpretability.[XGBoost]This review links RHA material science with AI, focusing on input selection, feature engineering, explainable, uncertainty-aware and physics-informed ML, and proposes an integrated framework: RHA characterization → predictive modeling → validation → durability/service-life → LCA-based multi-objective optimization, to move from trial-and-error to intelligent design of low-carbon RHA concrete.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://ijsrce.com/home/article/download/IJSRCE261053/IJSRCE261053first seen 2026-10-16 05:47:01 · last seen 2026-11-06 05:39:39
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。