A simplified iterative approach for testing the pulse derailment of light rail vehicles across a viaduct to near-fault earthquake scenarios

Chen, Lingkun; Liu, Peng; Zhu, Liming; Ding, Jing-Bo; Feng, Yulin; Moreu, Fernando

doi:10.1177/0954409720987410

Cited by 7 publications

(4 citation statements)

References 35 publications

(38 reference statements)

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“…Rail damages can lead to tremendous hazards of high-speed trains, and it is of great engineering significance for the damage diagnosis. 1–4 The steel rail failure is one typical kind of rail damage, and many researches have focused on diagnosing its fault to enhance the rail security and reliability 5,6…”

Section: Introductionmentioning

confidence: 99%

“…Rail damages can lead to tremendous hazards of highspeed trains, and it is of great engineering significance for the damage diagnosis. [1][2][3][4] The steel rail failure is one typical kind of rail damage, and many researches have focused on diagnosing its fault to enhance the rail security and reliability 5,6 The ultrasonic guided wave (UGW) has emerged as a promising tool for nondestructive testing due to its capability to propagate over long distances with minimal loss and sensitivity to both surface and subsurface defects. 7,8 The current diagnostic methods based on UGW are mainly of two kinds including mechanismbased diagnostic methods 9 and data driven intelligent diagnostic methods such as deep learning.…”

Section: Introductionmentioning

confidence: 99%

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A diagnostic framework with a novel simulation data augmentation method for rail damages based on transfer learning

Xie

Guo

Wang

et al. 2023

Structural Health Monitoring

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The ultrasonic guide wave (UGW) has good application prospects in steel rail damage diagnosis, but the features of the rail damage implied in the UGW are complex. Deep learning enables an end-to-end approach to fault diagnosis. Nevertheless, a large amount of diversity data is needed for training, whereas the ultrasonic wave guide signals of simulation and repeated experiments lack diversity. Therefore, in this paper, a diagnostic framework based on simulation and transfer learning for rail damage is developed to tackle the problems mentioned above. The proposed framework is based on deep learning with a simulation pretraining strategy to build convolutional neural network (CNN) models through parameter fine-tuning for damage diagnosis. Specifically, for the problem that the simulation data lacks diversity, a damage mechanism-based data diversity augmentation method is proposed; this obtains the diagnostic high-value simulation data including supporting features, and expanded the diversity of the simulation data. Adopting the proposed method of data augmentation and transfer learning (TL), a diagnostic model for rail damage utilizing augmented UGW signals is constructed. The finite element simulation data of UGW with damages at different locations and depths of rails are augmented to achieve the pretraining of CNN models, and the model transfer is performed with the experimental data of rails. Ultimately, through comparative studies it can be concluded that (1) The TL diagnostic framework makes full use of the finite element simulation data to realize the model pretraining. (2) The proposed data augmentation method realizes the diversity expansion of simulation data containing supporting features and ensures the efficient application of simulation data in model pretraining.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A diagnostic framework with a novel simulation data augmentation method for rail damages based on transfer learning

Xie

Guo

Wang

et al. 2023

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…All experimental works have their limitations, either due to the difficulties or ambiguity in filed investigations or measuring some quantities [27,28], mainly due to the complexity of the test process. Numerical simulations and field investigations are widely used to study the deformation and the stability mechanism of the landslide [29], and there are a few research studies attending to apply the integrated monitoring system to a single landslide and combine the numerical model results [30,31].…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis of the Shiliushubao Landslide Based on Deformation Characteristics and External Trigger Factors in the Three Gorges Reservoir

Luo

Ren

Bao

et al. 2021

Advances in Civil Engineering

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There exists the problem of landslide reactivation due to the seasonal fluctuation of rainfall and reservoir water level annually. Based on a large number of GPS monitoring data of the landslide mass after impoundment of the Three Gorges Reservoir in Shiliushubao landslide area, the relationship between the external trigger factors and slope stability could be obtained. A finite element calculation model has been established for the stability analysis of the Shiliushubao landslide after impoundment from January 2004 to October 2009. Through the deformation characteristics of the landslide, it is shown that the landslide exhibited a stepwise pattern on the whole, which developed faster after impoundment and slowed down in rainy seasons. The trend of the curve kept roughly opposite to the fluctuation of the safety factor. It suggested that the stability of the landslide mass was closely related to the seasonal fluctuation of the rainfall and the reservoir level, and the landslide deposits demonstrated to be reactive with them. The subject provides a certain reference value on the landslide stability analysis and the risk assessment within a similar engineering geological condition.

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“…e fitting method can be applied to more pile-soil conditions [18][19][20][21][22][23][24][25][26][27][28][29][30][31] and can simulate hysteresis damping, radiation damping, soil plugging effects, and various pile-soil nonlinear problems, but the analysis requires high-quality software and highly skilled personnel. erefore, more time is required on the machine for analysis and generally can only be completed when the test is completed and returned to the room.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Automatic Calculation Method of CASE Bearing Capacity

Liu

Wang

2021

Advances in Civil Engineering

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Because of the reliance on the empirical parameter CASE damping coefficient Jc, the high-strain CASE method is not recommended. The automatic bearing capacity calculation method RAU can avoid this, but it cannot be applied to friction piles. Based on the analysis of the automatic calculation method of bearing capacity RAU, considering the influence of pile side soil resistance unloading, this paper improved the RAU method through the influence of pile side soil damping on the velocity of the pile mass point during the stress wave propagation process. In this paper, we compare the collected static load test results of test piles with the improved automatic method results. The unary linear regression analysis is carried out with the help of statistical tools. The unary linear regression of the improved automatic method bearing capacity results on the static load test bearing capacity results is established. The improved automatic method solves the problem that the RAU method is only applicable to end-bearing piles and can be applied to the quality analysis and bearing capacity calculation of the high-strain curve of the driven pile.

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A simplified iterative approach for testing the pulse derailment of light rail vehicles across a viaduct to near-fault earthquake scenarios

Cited by 7 publications

References 35 publications

A diagnostic framework with a novel simulation data augmentation method for rail damages based on transfer learning

A diagnostic framework with a novel simulation data augmentation method for rail damages based on transfer learning

Stability Analysis of the Shiliushubao Landslide Based on Deformation Characteristics and External Trigger Factors in the Three Gorges Reservoir

Improvement of Automatic Calculation Method of CASE Bearing Capacity

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