ABSTRACT

The adoption of mechanistic-empirical approach to pavement design requires the use of resilient modulus of subgrade soils as a crucial input. The determination of in the laboratory is inexpedient due to the nature of the existing test protocols. This prompted the use of estimated values, which inadvertently has gained popularity lately. However, the accuracy of estimated values is questionable due to spatial variability of soil properties. This necessitated the aggressive search for robust and thorough approaches for predictive modelling of the . In the present study, a systematic approach was adopted for the descriptive analysis and estimation of . from routine soil properties using data from Long-Term Pavement Performance (LTPP) and considering the spatial variability of the soil properties. Descriptive analysis was executed using non-parametric correlation and principal component analysis (PCA), while the estimation was done using three machine learning methods which include gradient boosting regression (GBR), adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Based on the PCA, four factors which explained a total of 77.5% variance in the data had significant influence on the . These include the effect of moisture-induced changes on the soil consistency limits and physical condition, effect of the soil clay content, effect of the soil gradation and effect of the soil stress state. Various factors of the machine learning methods such as the learning rate, number of clusters and number of hidden layers had a significant effect on the prediction accuracy. The three machine learning methods were satisfactory for the prediction based on R² values which were generally above 0.9. Also, when considering spatial variability of routine soil properties, the GBR and ANFIS have a comparative advantage over the ANN, since they exhibited a high stability in the prediction for both the training and testing dataset.

摘要

采用机械经验方法进行路面设计需要使用弹性模量路基土壤作为关键投入。的确定由于现有测试协议的性质，在实验室中进行测试是不方便的。这提示使用估计价值观，最近在不经意间变得流行起来。但是估计的准确率由于土壤性质的空间变异性，值是有问题的。这需要积极寻找稳健和彻底的方法来预测建模. 在本研究中，采用系统的方法进行描述性分析和估计. 使用来自长期路面性能 (LTPP) 的数据并考虑土壤特性的空间变异性，从常规土壤特性中得出。使用非参数相关和主成分分析（PCA）进行描述性分析，而使用梯度增强回归（GBR）、自适应神经模糊推理系统（ANFIS）和人工神经网络三种机器学习方法进行估计。安）。基于主成分分析，解释数据中总方差为 77.5% 的四个因素对. 其中包括水分引起的变化对土壤稠度限制和物理条件的影响、土壤粘土含量的影响、土壤级配的影响和土壤应力状态的影响。机器学习方法的各种因素，如学习率、聚类数和隐藏层数对预测准确度。三种机器学习方法都令人满意基于通常高于 0.9 的R ²值进行预测。此外，在考虑常规土壤特性的空间变异性时，GBR 和 ANFIS 比 ANN 具有比较优势，因为它们在训练和测试数据集的预测中都表现出高稳定性。