Probabilistic method for evaluating the permanent strain of unbound granular materials under cyclic traffic loading

Probabilistic method for evaluating the permanent strain of unbound granular materials under cyclic traffic loading
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评估循环交通荷载下未结合颗粒材料永久应变的概率方法

DOI:
10.1016/j.conbuildmat.2020.118975
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发表时间:
2020-08
影响因子:
7.4
通讯作者:
Nie Rusong
Nie Rusong
中科院分区:
工程技术1区
文献类型:
--
作者:
Xu Fang;Zhai Bin;Leng Wuming;Yang Qi;Leng Huikang;Nie Rusong

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通过一系列不同含水量(w)、围压(σ3)和动偏应力(σd)的大型循环三轴试验,研究了铁路路基基床用非结合粒料(UGM)的累积永久应变(εp)特性。结果表明,围压的增加有效地抑制了εp的发展速率,稳定试件的ε p行为可分为初始快速增加阶段和随后的逐渐稳定阶段,各阶段材料应变的成因不同。从第一个加载循环累积的永久应变,即,模型参数a与动-静偏应力比η呈幂函数关系,表征εp增长率的模型参数b与η和w有较强的相关性。基于半对数预测模型和概率破坏理论,提出了一种考虑围压、动态偏应力、静态破坏偏应力和含水量影响的UGM ε p发展范围的概率方法。应用结果表明,所提出的概率模型可以得到可接受的UGM ε p预测值。
In this study, a series of large-scale cyclic triaxial tests with different water contents (w), confining pressures (σ3), and dynamic deviatoric stresses (σd) is performed to investigate the accumulative permanent strain (εp) behavior of an unbound granular material (UGM) used in the construction of railway subgrade beds. Results indicate that an increase in confining pressure effectively inhibits the development rate ofεp, and theεpbehavior of stable specimens can be categorized into an initial rapidly increasing stage and a subsequent gradual stabilization stage, with different causes of material strain at each stage. The permanent strain accumulated from the first loading cycle, i.e., the model parametera, demonstrates a power function relationship with the dynamic-static deviatoric stress ratio (η), and the model parameterb, which implies the increasing rate ofεp, is strongly correlated withηandw. A probabilistic method based on a semi-logarithmic prediction model and probabilistic failure theory is proposed to evaluate the development range ofεpof UGMs while considering the effects of the confining pressure, dynamic deviatoric stress, static failure deviatoric stress, and water content. Applications of the proposed method indicate that the developed probabilistic model can yield acceptableεppredictions of UGMs.
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