Normalised curvature square ratio for detection of ballast voids and pockets under rail track sleepers

Kaewunruen, Sakdirat; Janeliukštis, Rims; Freimanis, Andris; Goto, Keiichi

doi:10.1088/1742-6596/1106/1/012002

Cited by 6 publications

(5 citation statements)

References 24 publications

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“…The aspects of the structural health monitoring and diagnostics of the ballast support are presented in the studies [15][16][17][18][19][20][21][22][23][24]. The most recent studies are related to the track stiffness measurements using track-side or vehiclebased and track-based measurement systems.…”

Section: Introductionmentioning

confidence: 99%

“…The measurements were used for the estimation of the subgrade linear stiffness based on the beam on elastic foundation theory. Kaewunruen et al [17] have developed a curvature-based damage detection method for the identification of the ballast voids under railway track sleepers. This method is declared to be easily deployed in the field by using fiber Bragg grating strain sensors to measure strains for curvature analysis.…”

Section: Introductionmentioning

confidence: 99%

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Experimental investigation of the dynamic behavior of railway track with sleeper voids

2020

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The deterioration of the sleeper support on the ballasted track begins with the accumulation of sleeper voids. The increased dynamic loading in the voided zone and the ballast contact conditions cause the accelerated growth of the settlements in the voided zones, which results in the appearance of local instabilities like ballast breakdown, white spots, subgrade defects, etc. The recent detection and quantification of the sleeper voids with track-side and onboard monitoring can help to avoid or delay the development of local instabilities. The present paper is devoted to the study of the dynamic behavior of railway track with sleeper voids in the ballast breakdown zone. The result of the experimental track-side measurements of rail acceleration and deflection is presented. The analysis shows the existence of the dynamic impact during wheel entry in the voided zone. However, the measured dynamic impact is subjected to the bias of the track-side measurement method. Both the mechanism of the impact and the measurement aspects are explained by using the one-beam model on viscoelastic foundation. The void features in the dynamic behavior are analyzed for the purpose of track-side and onboard monitoring. A practical method of the void parameter quantification is proposed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental investigation of the dynamic behavior of railway track with sleeper voids

2020

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“…Clark et al [19] presented an application of non-destructive acoustic emission technology for damage detection in railway concrete sleepers. Kaewunruen [20] analysed the dynamic mode shape to evaluate curvature ratios under different types of ballast losses using the sleeper finite element model and then developed a curvature-based damage detection method to identify ballast voids under railway track sleepers. It also belongs to the track-side method.…”

Section: Introductionmentioning

confidence: 99%

Ballasted Track Behaviour Induced by Absent Sleeper Support and its Detection Based on a Convolutional Neural Network Using Track Data

Zhang

Tian

et al. 2023

Urban Rail Transit

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With development of the heavy-haul railway, the increased axle load and traction weight bring a significant challenge for the service performance and safety maintenance of the railway track. Conducting defect recognition on concrete sleepers and ballast using big data is vital. This paper focused on the detection of absent sleeper support in a ballasted track with an emphasis on the integration of model-based and data-driven methods. To this end, a mathematical model consisting of the wagon, track and wheel–rail contact subsystems was first established to acquire the necessary raw data for the data-driven method, in which the wagon was regarded as a 47-degree-of-freedom multi-body subsystem, and the track was treated as a multi-layer discrete-elastic support beam subsystem with absent sleeper support. Then, an architectural hierarchy of a three-layer convolutional neural network (TLCNN) was developed, which includes three convolutional layers and two pooling layers, and a method for reconstructing one-dimensional sleeper vertical displacement to a two-dimensional time–space matrix was also proposed. Thirdly, verification was carried out by comparing the simulation and experimental results to illustrate the accuracy and reliability of the mathematical model, and the dynamic behaviour of the track with absent sleeper support was investigated. Lastly, the established TLCNN was used to train the raw data of the sleeper vertical displacement and detect the existence of absent sleeper support. Results show that the integration of model-based and data-driven methods was a reliable and effective approach for the detection of absent sleeper support. The proposed TLCNN can acquire and extract robust characteristics in a noisy environment. To handle more complex recognition tasks and further improve performance, deeper CNN models and larger sample sizes should be preferentially considered in practical applications.

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“…In the field of structural damage identification based on strain mode information, the traditional method was to design a sensitive and accurate damage index to identify the damage in the tested structure. Kaewunruen et al [ 7 ] proposed a damage identification index based on curvature analysis based on the strain measured by the fiber Bragg grating strain sensor, which was used to identify the damaged cavity under railway sleeper. WL Bayissa et al [ 8 ] proposed spectral or mean strain energy (SSE) for structural damage identification of plate-like structures, and a large number of numerical simulation studies show that SSE index had high sensitivity to damage and good identification performance.…”

Section: Introductionmentioning

confidence: 99%

SDFormer: A Novel Transformer Neural Network for Structural Damage Identification by Segmenting the Strain Field Map

Xing

et al. 2022

Sensors

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Damage identification is a key problem in the field of structural health monitoring, which is of great significance to improve the reliability and safety of engineering structures. In the past, the structural strain damage identification method based on specific damage index needs the designer to have rich experience and background knowledge, and the designed damage index is hard to apply to different structures. In this paper, a U-shaped efficient structural strain damage identification network SDFormer (structural damage transformer) based on self-attention feature is proposed. SDFormer regards the problem of structural strain damage identification as an image segmentation problem, and introduces advanced image segmentation technology for structural damage identification. This network takes the strain field map of the structure as the input, and then outputs the predicted damage location and level. In the SDFormer, the low-level and high-level features are smoothly fused by skip connection, and the self-attention module is used to obtain damage feature information, to effectively improve the performance of the model. SDFormer can directly construct the mapping between strain field map and damage distribution without complex damage index design. While ensuring the accuracy, it improves the identification efficiency. The effectiveness and accuracy of the model are verified by numerical experiments, and the performance of an advanced convolutional neural network is compared. The results show that SDFormer has better performance than the advanced convolutional neural network. Further, an anti-noise experiment is designed to verify the anti-noise and robustness of the model. The anti-noise performance of SDFormer is better than that of the comparison model in the anti-noise experimental results, which proves that the model has good anti-noise and robustness.

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Normalised curvature square ratio for detection of ballast voids and pockets under rail track sleepers

Cited by 6 publications

References 24 publications

Experimental investigation of the dynamic behavior of railway track with sleeper voids

Experimental investigation of the dynamic behavior of railway track with sleeper voids

Ballasted Track Behaviour Induced by Absent Sleeper Support and its Detection Based on a Convolutional Neural Network Using Track Data

SDFormer: A Novel Transformer Neural Network for Structural Damage Identification by Segmenting the Strain Field Map

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