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ジオポリマー建設材料のための機械学習とライフサイクルアセスメント:前駆体の変動性からスケーラブルな低炭素展開まで

Machine Learning and Life Cycle Assessment for Geopolymer Construction Materials: From Precursor Variability to Scalable Low-Carbon Deployment (原題)

Justin Kinda, Tégawendé Justin ZAIDA, Etienne Malbila, Fati Zoma

Cleaner Environmental Systems📚 査読済 / ジャーナル2026-10-01#エネルギー転換Origin: Global経営インパクト: コスト削減対象セクター: construction
DOI: 10.1016/j.cesys.2026.100492
原典: https://doi.org/10.1016/j.cesys.2026.100492

🤖 gxceed AI 要約

日本語

建設脱炭素の鍵となるジオポリマー材料について、機械学習サロゲートモデル・LCA・多目的最適化・地域展開ロジスティクスを統合した5段階フレームワークを提案。151件の文献レビューにより、最適化されたフライアッシュ-スラグ系でOPC比30〜70%のGWP削減が現実的と結論。西アフリカでは現地産バインダーが輸入OPCとコスト同等以上となる可能性を示す。

English

This review proposes a five-stage framework coupling ML surrogate modelling, LCA, multi-objective optimization, and regional deployment logistics for geopolymer construction materials. Synthesizing 151 sources, it finds 30-70% GWP reductions versus OPC are realistic for optimized fly ash-slag systems, while >80% claims rely on favourable allocation. A West Africa case shows locally produced binders can reach cost parity with imported OPC.

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

日本では建設分野の脱炭素とScope 3排出削減が喫緊の課題であり、MLとLCAを組み合わせた材料評価手法は国内建設大手の調達・設計判断に応用可能。ただし西アフリカ文脈が中心のため、日本向けには方法論的示唆の抽出が必要。

In the global GX context

Globally, this contributes to the growing intersection of AI and LCA for hard-to-abate sectors, offering a replicable framework for embodied-carbon accounting in construction. It aligns with ISSB/CSRD disclosure of Scope 3 and product-level carbon footprints, though its empirical focus on West Africa limits direct transferability to other regions.

👥 読者別の含意

🔬研究者:MLとLCAを統合した材料設計・展開評価の方法論的枠組みと、ジオポリマーLCAの報告プロトコル統一案を提供。

🏢実務担当者:建設材料の調達・設計において、低炭素代替材のコストと炭素削減ポテンシャルを評価する際の実践的枠組みとして参考になる。

🏛政策担当者:資源制約下での低炭素建設材料の地域展開政策を設計する際、コスト競争力と炭素削減のトレードオフを定量的に評価する手法を提示。

📄 Abstract(原文)

Geopolymer materials remain a heavily studied but under-deployed pathway to decarbonize construction, and the literature lacks an integrated framework translating laboratory results into deployment decisions, particularly for resource-constrained economies. This critical review synthesizes machine-learning (ML) surrogate modelling, life cycle assessment (LCA), multi-objective optimization (MOO), and regional deployment logistics. Following a PRISMA-adapted protocol, 151 sources were evaluated from 1 424 unique records against a seven-criterion quality checklist. On this evidence base we conceptually propose and illustrate a five-stage methodological framework—coupling ML prediction, uncertainty propagation, LCA-constrained MOO, and a Performance–Carbon–Cost–Readiness deployment matrix. Each stage has been demonstrated independently in the cited literature; their joint operation is advocated as a research direction rather than executed here on new data. We further expose five recurring methodological inconsistencies in geopolymer LCA studies and propose a harmonized reporting protocol. Our quality-weighted synthesis indicates that calibrated cradle-to-gate global warming potential (GWP) reductions of 30–70% versus ordinary Portland cement (OPC) are realistic for optimized fly ash–slag systems under a cradle-to-gate boundary, an equal-strength functional unit, and system-expansion or economic allocation, whereas claims exceeding 80% rely on favourable allocation assumptions traceable to specific low-transparency studies. A regional deployment case for West Africa, supported by a unit-cost model with Monte Carlo sensitivity analysis, indicates that locally produced calcined-laterite and rice-husk-ash-silicate binders reach cost parity or better with imported OPC in the majority of simulated scenarios (2026 USD; cradle-to-gate binder cost benchmarked against imported OPC delivered to Burkina Faso and Mali), extending to a 15–30% advantage under favourable logistics, with competitiveness most sensitive to the imported-alkali price. Six prioritized research directions are formulated to operationalize this framework, shifting focus from single-objective strength optimization to multidimensional, regionally anchored deployment.

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