In the field of pavement engineering, the determination of the mechanical characteristics is one of the essential processes for reliable material design and highway sustainability. Early determination of the mechanical characteristics of pavement is essential for road and highway construction and maintenance. Tensile strength (TS), compressive strength (CS), and flexural strength (FS) of roller-compacted concrete pavement (RCCP) are crucial characteristics. In this research, the classification-based regression models random forest (RF), M5rule model tree (M5rule), M5prime model tree (M5p), and chi-square automatic interaction detection (CHAID) are used for simulation of the mechanical characteristics of RCCP. A comprehensive and reliable dataset comprising 621, 326, and 290 data records for CS, TS, and FS experimental cases was extracted from several open sources in the literature. The mechanical properties are determined based on influential input combinations that are processed using principle component analysis (PCA). The PCA method specifies that volumetric/weighted content forms of experimental variables (e.g., coarse aggregate, fine aggregate, supplementary cementitious materials, water, and binder) and specimens’ age are the most effective inputs to generate better performance. Several statistical metrics were used to evaluate the proposed classification-based regression models. The RF model revealed an optimistic classification capacity of the CS, TS, and FS prediction of the RCCP in comparison with the CHAID, M5rule, and M5p models. Monte-Carlo simulation was used to verify the results in terms of the uncertainty and sensitivity of variables. Overall, the proposed methodology formed a reliable soft computing model that can be implemented for material engineering, construction, and design.

中文翻译：

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基于分类的回归模型预测碾压混凝土路面的力学性能

在路面工程领域，机械性能的确定是可靠的材料设计和高速公路可持续性的重要过程之一。尽早确定路面的机械特性对于道路和高速公路的建设和维护至关重要。碾压混凝土路面（RCCP）的抗张强度（TS），抗压强度（CS）和抗弯强度（FS）是关键特性。在这项研究中，基于分类的回归模型随机森林（RF），M5rule模型树（M5rule），M5prime模型树（M5p）和卡方自动交互检测（CHAID）用于模拟RCCP的机械特性。全面而可靠的数据集，包含621、326和290个CS，TS，FS实验案例是从文献中的几个公开来源中提取的。机械性能基于使用主成分分析（PCA）处理的有影响的输入组合来确定。PCA方法指定实验变量的体积/加权含量形式（例如，粗骨料，细骨料，补充胶凝材料，水和粘结剂）和标本的年龄是产生更好性能的最有效输入。几种统计指标被用来评估所提出的基于分类的回归模型。与CHAID，M5rule和M5p模型相比，RF模型揭示了RCCP的CS，TS和FS预测的乐观分类能力。蒙特卡洛模拟用于验证变量不确定性和敏感性的结果。总体而言，所提出的方法论形成了可用于材料工程，构造和设计的可靠的软计算模型。