Numerical simulation and novel methodology on resilient modulus for traffic loading on road embankment

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 (gamma(d)), uniformity coefficient (C-u), percent passing a No. 200 sieve (#200), unconfined compressive strength (q(u)), plasticity index (PI), confining stress (sigma(o)), deviator stress (sigma(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.

Automated summary

This study investigates the prediction of resilient modulus using Random Forest and M5P tree regression models, as well as examines deformation characteristics of subbase materials and subgrade soil under repeated loading through numerical simulation. The performance of the models was evaluated comprehensively using statistical criteria, showing that the Random Forest model outperforms the M5P model in terms of training performances and prediction accuracies.