Intelligent design of low-carbon asphalt mixtures with enhanced cracking and rutting resistance
ひび割れ耐性とわだち掘れ耐性を向上させた低炭素アスファルト混合物の知的設計 (AI 翻訳)
Bingyan Cui, Hao Wang
🤖 gxceed AI 要約
日本語
本研究では、物理制約付きニューラルネットワーク（PCNN）と進化的アルゴリズム（NSGA-III）を統合した知的フレームワークを開発し、低炭素アスファルト混合物の多目的最適化を実現した。PCNNはひび割れ・わだち掘れ指数を高精度に予測し、TOPSIS法により最適配合を選定。結果、従来設計より炭素排出量とコストを削減しつつ、耐性能を向上させた。
English
This study develops an intelligent optimization framework integrating physics-constrained neural networks (PCNN) and NSGA-III to design low-carbon asphalt mixtures. The PCNN accurately predicts cracking and rutting indices, and TOPSIS selects optimal mixtures, achieving reduced carbon emissions and costs while enhancing performance.
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化が進んでおり、本手法はアスファルト舗装の低炭素設計に直接貢献できる。ただし、日本の気候や材料仕様への適用には追加検証が必要。
In the global GX context
This paper contributes to global GX efforts in sustainable infrastructure by providing a data-driven method to reduce embodied carbon in asphalt pavements. The integration of physics-informed AI with multi-objective optimization is a novel approach applicable to other low-carbon material design challenges.
👥 読者別の含意
🔬研究者:The PCNN+NSGA-III framework offers a new paradigm for multi-objective optimization of low-carbon materials with physical consistency.
🏢実務担当者:Pavement engineers can use this approach to design mixtures that balance performance, emissions, and cost using readily available data.
🏛政策担当者:The methodology supports sustainability standards for infrastructure by quantifying trade-offs between performance and carbon reduction.
📄 Abstract（原文）
Balancing the performance of asphalt mixtures with their environmental and economic impacts is an important goal in sustainable pavement engineering. This study establishes an intelligent optimization framework that integrates physics-constrained neural networks (PCNN) with the non-dominated sorting genetic algorithm III (NSGA-III) to address this multi-objective problem. PCNN models were developed to predict cracking and rutting indices by embedding physical constraints into the training process. NSGA-III was applied to generate Pareto-front solutions that capture trade-offs among cracking resistance, rutting resistance, carbon emissions, and costs. To identify the most balanced mixture, the technique for order of preference by similarity to ideal solution (TOPSIS) was applied to rank the solutions along the Pareto front. The results show that the proposed PCNN model not only achieves higher predictive accuracy than traditional machine learning models, but also improves the generalization and physical consistency of asphalt performance evaluation. The optimized mix designs show better cracking and rutting resistance while also lowering embodied carbon emissions and costs. A clear trade-off between cracking resistance and rutting resistance is observed. The level of improvement varies depending on asphalt binder grade and nominal maximum aggregate size. Overall, this study establishes an integrated and data-driven methodology for low-carbon asphalt mixture design.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.susmat.2026.e02019first seen 2026-05-17 05:37:21 · last seen 2026-05-21 04:46:48
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