Ghafari Sepehr, Ehsani Mehrdad, Ranjbar Sajad, Nazari Mohammad Nabi, Moghadas Nejad Fereidoon
School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, UK.
Department of Civil & Environmental Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Sci Rep. 2025 Jul 1;15(1):20376. doi: 10.1038/s41598-025-08404-5.
Determining mixed mode fracture parameters asphalt concrete mixtures remains an engineering challenge due to non-homogeneity and inelasticity of the material. In this research, a study was conducted to determine the low-temperature R-curves of unmodified and crumb rubber modified Hot Mix Asphalt (HMA) under mode I and mixed-mode (I/II) loading conditions. Single edge notched beam (SE(B)) testing was employed to collect data, and three key fracture parameters-cohesive energy, energy rate, and fracture energy-were extracted to represent different stages of fracture and crack propagation. Within the scope of this study, it was observed that for the AC 85/100 paving grade bitumen, a temperature of - 20 °C serves as a critical temperature, shifting fracture from quasi-brittle to brittle. At this temperature, the stable crack growth region in the R-curves significantly shrinks, causing abrupt specimen failure. The incorporation of 20% crumb rubber demonstrated favorable material characteristics, with a progressively rising R-curve even during the unsfi crack propagation phase. The central goal of this research is to establish prediction models for the mixed-mode (I/II) crack propagation parameters G, G, and G. The features selected for modeling are G, G, and G (mode I), percentage of crumb rubber, type of aggregate, binder content, nominal maximum aggregate size, temperature, and normalized offset ratio. Two dataset configurations were used: dataset 1 contains all entries, while dataset 2 excludes G, G, and G (mode I). Five machine learning techniques, Regression, Multi-Gene Genetic Programming (MGGP), Support Vector Regression (SVR), Random Forest, and Artificial Neural Networks were employed to predict three key fracture parameters. Although slightly less accurate than SVR and Random Forest, MGGP offers the key advantage of yielding explicit mathematical expressions for crack propagation prediction. The R index for the MGGP model in Dataset 1 was 0.93 for G, 0.94 for G, and 0.92 for G. For dataset 2, the indices were 0.89, 0.93, and 0.88, respectively.
由于材料的非均匀性和非弹性，确定沥青混凝土混合料的混合模式断裂参数仍然是一项工程挑战。在本研究中，开展了一项研究，以确定未改性和橡胶粉改性热拌沥青（HMA）在I型和混合模式（I/II）加载条件下的低温R曲线。采用单边切口梁（SE（B））试验来收集数据，并提取了三个关键断裂参数——内聚能、能量率和断裂能，以代表断裂和裂纹扩展的不同阶段。在本研究范围内，观察到对于AC 85/100道路石油沥青，-20°C的温度是一个临界温度，使断裂从准脆性转变为脆性。在此温度下，R曲线中的稳定裂纹扩展区域显著缩小，导致试样突然破坏。掺入20%的橡胶粉表现出良好的材料特性，即使在不稳定裂纹扩展阶段，R曲线也逐渐上升。本研究的核心目标是建立混合模式（I/II）裂纹扩展参数G、G和G的预测模型。用于建模选择的特征是G、G和G（I型）、橡胶粉百分比、集料类型、粘结剂含量、标称最大集料尺寸、温度和归一化偏移率。使用了两种数据集配置：数据集1包含所有条目，而数据集2排除G、G和G（I型）。采用了五种机器学习技术，即回归、多基因遗传编程（MGGP）、支持向量回归（SVR）、随机森林和人工神经网络来预测三个关键断裂参数。尽管MGGP的准确性略低于SVR和随机森林，但它的主要优点是能给出裂纹扩展预测的显式数学表达式。数据集1中MGGP模型的G的R指数为0.93，G为0.94，G为0.92。对于数据集2，指数分别为0.89、0.93和0.88。
