Abstract

Critical dynamic stress (σcri) and accumulative plastic strain (εp) are primary indicators regarding the dynamic stability of unbound granular materials (UGMs). This study aims to seek an effective method to evaluate the dynamic stability of UGMs used in railway subgrades. First, the dynamic characteristics of an UGM used in railway subgrade bed construction were investigated by performing a series of large-scale cyclic triaxial tests, with the results showing that εp versus cycle number (N) curves can be categorized into stable, failure, and critical patterns. Grey relational analyses were then established, where the analyzed results demonstrated that the εp–N curve pattern and final accumulative plastic strain (εs) of the stable curves are strongly correlated with the moisture content (w), confining pressure (σ3), and dynamic deviator stress (σd). The analyzed grey relational grades distributed in a narrow range of 0.72 to 0.81, indicating that w, σ3, and σd have similar degrees of importance on determining the εp–N curve patterns and the values of εs of the UGM. Finally, a data processing method using a back-propagation (BP) neural network is introduced to analyze the test data, and an empirical approach is developed to evaluate the σcri (considering the effects of σ3 and w) and εs (considering the effects of σ3, w, and σd) of the UGM. The analyzed results illustrated that the developed method can effectively reflect the linear/non-linear relationships of σcri and εs with respect to σ3 and/or σd. The σcri approximately increases linearly with increasing σ3, and a simple empirical formula is proposed for the σcri. In addition, εs and its variation rate increase non-linearly with increasing σd but decrease non-linearly as σ3 increases.

Highlights

  • The serviceability of railway tracks is strongly associated with the dynamic stability of unbound granular layers atop weaker layers of the subgrades, of which the critical dynamic stress and accumulative plastic/permanent stain are primary indicators regarding the dynamic stability of unbound granular materials (UGMs) [1,2,3,4,5,6]

  • The mean square errors (MSE) of the trained BP neural network regarding the variation of parameter B are presented in Figure 8, where the results showed that the MSE attained the minimum value when B = 3, three neurons in the hidden layer were adopted in the following analyses

  • The critical dynamic stress and accumulative plastic strain are primary indicators regarding the dynamic stability of unbound granular materials (UGMs)

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Summary

Introduction

The serviceability of railway tracks is strongly associated with the dynamic stability of unbound granular layers atop weaker layers of the subgrades, of which the critical dynamic stress (σcri) and accumulative plastic/permanent stain (εp) are primary indicators regarding the dynamic stability of unbound granular materials (UGMs) [1,2,3,4,5,6]. The cyclic triaxial test is an efficient way to study the critical dynamic stress of subgrade materials [20,21,22,23]. Xu et al [6] presented an empirical method to predict the σcri by introducing the concept of critical stress ratio; Zhai et al [16] predicted the σcri of UGMs as a function of confining pressure based on results of cyclic triaxial tests of specimens with optimal moisture content. Several evaluation models of σcri have been proposed on the basis of cyclic triaxial tests; the factors considered in the models regarding the σcri of UGMs are incomplete, and systematic studies of σcri considering the effects of both moisture content and confining pressure are desirable

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