ABSTRACT

Accurate determination of the resilient modulus (M_R) of subbase materials and subgrade soil is a major concern and an essential criterion in the design process of the flexible pavement. The experimental determination of M_R involves a challenging process that requires ordinarily very difficult test procedures and extreme cautions and labour. For this reason, soft computing approaches and numerical simulation techniques are becoming more popular and have increasing importance. Most of the current studies cannot provide flexible usage and consistent prediction of the M_R for practical engineering. In the present study, it is intended to investigate the bagged and unbagged with Random Forest (RF) and M5P tree regression models for forecasting the M_R. On the other hand, the numerical simulation established to examine the effect of soil properties on deformation characteristics of subbase materials and subgrade soil subjected to repeated loading. A database employed for developing the models consists of a large amount of data collected from various published research. It includes routine properties of soil such as the dry unit weight (γ_d), uniformity coefficient (C_u), percent passing a No. 200 sieve (#200), unconfined compressive strength (q_u), plasticity index (PI), confining stress (σ_o), deviator stress (σ_d), degree of saturation (S_r) water content (w) and optimum water content (w_opt). The performance of models was evaluated comprehensively by some statistical criteria. The results revealed that the models are a fairly promising approach for the prediction of M_R and capable of representing the complex relationship between M_R and fundamental material properties. The statistical performance evaluations showed that the RF model significantly outperforms the M5P models in the sense of training performances and prediction accuracies. The numerical analysis showed that the mechanical parameter like elastic modulus is the dominant parameter on the behaviour of the materials subjected to repeated loading.

摘要

准确测定底基层材料和路基土的弹性模量（M _{R ）是柔性路面设计过程中的一个主要问题和基本标准。}M _R的实验测定涉及一个具有挑战性的过程，需要通常非常困难的测试程序以及极其谨慎和劳动。出于这个原因，软计算方法和数值模拟技术正变得越来越流行并且越来越重要。目前的大多数研究不能提供灵活的使用和一致的M _R预测用于实用工程。在本研究中，旨在使用随机森林 (RF) 和 M5P 树回归模型研究袋装和未装袋以预测M _R。另一方面，建立数值模拟研究土体性质对重复荷载作用下底基材料和路基土变形特性的影响。用于开发模型的数据库包含从各种已发表的研究中收集的大量数据。它包括土壤的常规特性，例如干重 (γ _d )、均匀性系数 (C _u )、通过 200 号筛 (#200) 的百分比、无侧限抗压强度 (q _u )、塑性指数 (PI)、围压（σ_o )、偏应力 (σ _d )、饱和度 (S _r ) 含水量 (w) 和最佳含水量 (w _opt )。通过一些统计标准综合评估模型的性能。结果表明，这些模型是一种相当有前途的预测M _R的方法，并且能够表示M _R之间的复杂关系。和基本的材料特性。统计性能评估表明，RF 模型在训练性能和预测精度方面明显优于 M5P 模型。数值分析表明，弹性模量等力学参数是影响材料反复加载行为的主要参数。