Identification of ballast condition using SmartRock and pattern recognition

Zeng, Kun; Qiu, Tong; Bian, Xuecheng; Xiao, Ming; Huang, Hai

doi:10.1016/j.conbuildmat.2019.06.049

Cited by 43 publications

(8 citation statements)

References 26 publications

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“…Around the world, researchers are investigating the possibility of early detection of imperfections in "Infrastructure" subsystem and improving maintenance efficiency [12][13][14][15][16][17][18][19][20]. The authors of this paper are engaged in the development of methodology for nondestructive inspection of superstructure and substructure condition (Figure 5).…”

Section: Discussionmentioning

confidence: 99%

Improvement recommendations for railway infrastructure maintenance

et al. 2020

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Based on the railway network performance, Infrastructure Manager is obligated to define the Maintenance plan for railway infrastructure, which contains corresponding values for intervention limits and alert limits. This paper considers vehicle response to track excitation due to the rail defect (code 2202 according to defect classification). It is indicated that theoretical models and acceleration measurements could be used to assess the quality of track geometry and ride comfort. The importance of early detection of irregularities of superstructure and substructure was emphasized. Moreover, the importance of inspection and preventive maintenance on the modern railway infrastructure was considered. According to the previous, the authors recommend non-destructive methods for inspection and early detection of irregularities of railway infrastructure.

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Section: Discussionmentioning

confidence: 99%

Improvement recommendations for railway infrastructure maintenance

et al. 2020

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“…Also, ref. [114] adopted similar SmartRocks to evaluate the ballast condition. Reference [115] 3D printed ballast particles with/without predefined fissure using photosensitive resin material and gypsum powder.…”

Section: Supporting Layersmentioning

confidence: 99%

State-of-the-Art Review on Additive Manufacturing Technology in Railway Infrastructure Systems

Kaewunruen

2021

J. Compos. Sci.

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Additive manufacturing technologies, well known as three-dimensional printing (3DP) technologies, have been applied in many industrial fields, including aerospace, automobiles, shipbuilding, civil engineering and nuclear power. However, despite the high material utilization and the ability to rapidly construct complex shaped structures of 3D printing technologies, the application of additive manufacturing technologies in railway track infrastructure is still at the exploratory stage. This paper reviews the state-of-the-art research of additive manufacturing technologies related the railway track infrastructure and discusses the challenges and prospects of 3D printing technology in this area. The insights will not only help the development of 3D printing technologies into railway engineering but also enable smarter railway track component design and improve track performance and inspection strategies.

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“…Particle movement is a meso-scale manifestation of inter-particle contact behavior of ballast assemblies within the ballast bed; therefore, investigating the meso-scale movement characteristics of ballast particles may emerge as a promising, effective alternative to diagnose and identify the mud-pumping problem of ballasted tracked. The use of motion sensors (termed as "SmartRocks") has been reported in the literature to directly capture real-time movement of ballast particles and then evaluate the field performance of ballasted trackbed under different in-service conditions [43][44][45][46][47]. The applications of such so-called SmartRock sensors in effective and accurate measurements of the vibrational responses of unbound aggregate particles including railroad ballasts were demonstrated in laboratory scaled model tests and triaxial tests [43,[48][49][50].…”

Section: Introductionmentioning

confidence: 99%

Characterizing Particle-Scale Acceleration of Mud-Pumping Ballast Bed of Heavy-Haul Railway Subjected to Maintenance Operations

Wang

Xiao

et al. 2022

Sensors

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Fouling and mud-pumping problems in ballasted track significantly degrade serviceability and jeopardize train operational safety. The phenomenological approaches for post hoc forensic investigation and remedies of mud pumps have relatively been well studied, but there still lacks studies on inherent mechanisms and ex ante approaches for early-age detection of mud pumps. This paper was aimed to exploring the feasibility of using particle acceleration responses to diagnose and identify early-age mud-pumping risks in real-world field applications. The innovative wireless sensors with 3D-printed shells resembling real shape of ballast particles were instrumented in the problematic railway section to monitor ballast particle movement prior to, during, and after maintenance operations, respectively. The real-time particle-scale acceleration data of ballast bed under both degraded and maintenance-restored clean conditions were recorded. The time histories, power spectra, and marginal spectra of 3D acceleration were comparatively analyzed. The results showed the 3D acceleration of ballast particles underneath rail-supporting tie plates displayed relatively clear periodicity of about 0.8 s with adjacent bogies regarded as a loading unit. The tamping operation was effective for compacting ballast bed laterally and improving the lateral interlocking of ballast particles, whereas the stabilizing operation was effective mainly in the lateral direction and for ballast particles underneath the sleepers. The mud pumps caused intensive particle-scale acceleration, and ballast particles underneath the sleepers were affected more severely than those in between adjacent sleepers. The ballast particles directly underneath tie plates exhibit dramatic acceleration variations due to maintenance operations as compared to those in other positions studied; hence, it seems promising to use particle-scale acceleration underneath tie plates as readily-implementable indicators for smart in-service track health monitoring.

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Identification of ballast condition using SmartRock and pattern recognition

Cited by 43 publications

References 26 publications

Improvement recommendations for railway infrastructure maintenance

Improvement recommendations for railway infrastructure maintenance

State-of-the-Art Review on Additive Manufacturing Technology in Railway Infrastructure Systems

Characterizing Particle-Scale Acceleration of Mud-Pumping Ballast Bed of Heavy-Haul Railway Subjected to Maintenance Operations

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