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On the selection of the most plausible non-linear axial stress–strain model for railway ballast under different impulse magnitudes
Structural Health Monitoring ( IF 6.6 ) Pub Date : 2021-07-29 , DOI: 10.1177/14759217211033968
Mujib Olamide Adeagbo 1 , Heung-Fai Lam 1, 2 , Qin Hu 3, 4
Affiliation  

The effective maintenance and health monitoring of ballasted railway tracks, which involves the determination of differential settlement, track support stress and stiffness, and the strain-hardening property of ballast, is essential. The vertical stress–strain behavior of the ballast layer is primarily responsible for the irrecoverable strains and settlements in tracks, leading to further track degradation. This article reports the development of a series of applicable yet simple uniaxial models and the selection of the most plausible one for capturing the behavior of vertical stresses and strains in ballasts utilizing a set of measured vibration data of the rail–sleeper–ballast system from a Bayesian perspective. From the literature, the dynamic behavior of ballast can be divided into linear and non-linear regions. Under small amplitude vibration, the stress–strain property is linear and elastic, while the behavior becomes non-linear and inelastic once the elasticity limit is exceeded. By integrating the linear phase to some well-known non-linear engineering material laws, a list of new ballast stress–strain model classes was developed. An enhanced Markov chain Monte Carlo–based Bayesian scheme was utilized to explicitly handle the uncertainties in the model updating process, while the Bayesian model class selection method was employed to select the most plausible ballast stress–strain model class under the prevailing system conditions. The proposed methodology was verified using three sets of measured acceleration data from impact hammer tests on an in situ sleeper with simulated ballast damage. The obtained results suggest that the linear-elastic model is sufficient for small amplitude vibrations, while the modified Voce model is the most plausible amongst the investigated model classes for high impact load. The results also demonstrate the importance of the non-linear ballast model in ballast damage identification and the potential applicability of the selected ballast model in field track monitoring.



中文翻译:

不同冲击量下铁路道碴最合理非线性轴向应力应变模型的选择

对有碴铁路轨道进行有效的维护和健康监测,包括确定不均匀沉降、轨道支撑应力和刚度以及道碴的应变硬化性能,是必不可少的。道碴层的垂直应力应变行为是造成轨道中不可恢复的应变和沉降的主要原因,导致轨道进一步退化。本文报告了一系列适用但简单的单轴模型的开发,并选择了最合理的模型来捕捉道碴中垂直应力和应变的行为,利用一组轨道 - 枕木 - 道砟系统的测量振动数据贝叶斯视角。从文献来看,镇流器的动态行为可分为线性区域和非线性区域。在小振幅振动下,应力应变特性是线性和弹性的,而一旦超过弹性极限,行为就会变得非线性和非弹性。通过将线性相位与一些著名的非线性工程材料定律相结合,开发了一系列新的镇流器应力应变模型类。基于增强的马尔可夫链蒙特卡罗贝叶斯方案被用于明确处理模型更新过程中的不确定性,而贝叶斯模型类选择方法用于在当前系统条件下选择最合理的压载应力-应变模型类。使用三组测量加速度数据验证了所提出的方法,这些数据来自对具有模拟压载损坏的原位枕木的冲击锤测试。获得的结果表明,线弹性模型足以用于小振幅振动,而修改后的 Voce 模型在所研究的高冲击载荷模型类别中是最合理的。结果还证明了非线性道碴模型在道碴损伤识别中的重要性以及所选道碴模型在现场轨道监测中的潜在适用性。

更新日期:2021-07-30
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