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GPT-5.5が生成した低炭素モルタル配合設計の実験的検証

Experimental Validation of GPT-5.5-Generated Low-Carbon Mortar Mixture Designs (原題)

Jun-Cheol Lee

Buildings📚 査読済 / ジャーナル2026-09-01#AI×ESG経営インパクト: コスト削減対象セクター: construction
DOI: 10.3390/buildings16173493
原典: https://doi.org/10.3390/buildings16173493

🤖 gxceed AI 要約

日本語

本研究は、GPT-5.5が生成した低炭素モルタル配合の実現可能性を実験的に検証した。60%のGGBFS置換により、セメント消費量60%、体積炭素約56%削減を達成しつつ、28日強度40MPaを満たした。GPT-5.5は予備設計の意思決定支援ツールとして有用だが、実験検証の必要性が示された。

English

This study experimentally validates low-carbon mortar mixtures generated by GPT-5.5. With 60% GGBFS replacement, it achieved 60% cement reduction and ~56% embodied carbon reduction while meeting 28-day strength of 40 MPa. GPT-5.5 is useful as a decision-support tool for preliminary design, but experimental validation remains essential.

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

日本の建設業界では、カーボンニュートラル達成に向けてセメント代替材料の活用が注目されており、本研究成果はAIを活用した低炭素材料設計の可能性を示す。SSBJ開示やサプライチェーン排出量削減の観点からも、建設資材の環境負荷低減は重要であり、日本のゼネコンや建材メーカーにとって参考になる。

In the global GX context

Globally, the construction sector faces pressure to reduce embodied carbon, aligning with ISSB and CSRD disclosure trends. This study demonstrates AI's potential in designing low-carbon concrete mixtures, offering a scalable approach for preliminary mix design that can support corporate sustainability targets and transition finance criteria.

👥 読者別の含意

🔬研究者:AIを材料設計に応用する際の実験検証の重要性と、GPT-5.5の予測精度の限界を示す実証データを提供。

🏢実務担当者:建設会社や建材メーカーは、AI生成の低炭素配合を予備設計に活用し、CO2削減目標達成に役立てられる。

🏛政策担当者:建設分野の脱炭素政策において、AI支援設計の可能性と標準化の必要性を示唆する。

📄 Abstract（原文）

Reducing Portland cement consumption is an effective strategy for lowering the environmental impact of cementitious materials; however, designing low-carbon mortar mixtures remains a complex engineering task. Inadequate proportioning of low-carbon cementitious mixtures may result in unsuitable workability or uncertain strength development, potentially causing difficulties in handling, placing, and compaction and affecting the consistency and quality of materials used in construction. This study experimentally investigated whether GPT-5.5 can independently generate technically feasible ordinary Portland cement (OPC)–ground granulated blast-furnace slag (GGBFS)-based low-carbon mortar mixtures that satisfy predefined engineering requirements under standard laboratory conditions. A structured engineering prompt specifying target compressive strength, mortar flow, available materials, and design constraints was submitted to GPT-5.5 in three independent conversations. The generated mixtures were manufactured without manual modification and evaluated through mortar flow, 7- and 28-day compressive strength, and mixture-based embodied-carbon assessments. Across the three independent conversations conducted in this study, GPT-5.5 proposed similar mixtures incorporating 60% GGBFS as a replacement for OPC. Although the model underestimated mortar flow and overestimated 7-day compressive strength, all generated mixtures were successfully manufactured without segregation or bleeding and achieved the target 28-day compressive strength of 40 MPa. Compared with the reference mortar, the AI-generated mixtures reduced Portland cement consumption by 60%, embodied carbon by approximately 56%, and embodied carbon intensity by approximately 50–53%. These findings demonstrate that GPT-5.5 can generate technically feasible low-carbon mortar mixtures for preliminary engineering design. However, experimental validation remains essential because the model should be regarded as a decision-support tool rather than a quantitative predictor of engineering performance.

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