Behavior Analysis and Early Warning of Girder Deflections of a Steel-Truss Arch Railway Bridge under the Effects of Temperature and Trains: Case Study

Zhao, Hanwei; Ding, Youliang; Nagarajaiah, Satish; Li, Aiqun

doi:10.1061/(asce)be.1943-5592.0001327

Cited by 67 publications

(40 citation statements)

References 24 publications

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“…The optimal decomposition scale of the wavelet transform can be determined by selecting an appropriate N which makes f s /2 N +1 slightly larger than f t and f s /2 N +2 smaller than f t . The advantage analysis and method validation of the strain decomposition by the wavelet transform can be found in Zhao et al 16 …”

Section: Mapping Relation Between Multi‐source Bridge Monitoring Datamentioning

confidence: 99%

“…Parts of them develop the data‐driven methods merging of the probability, mechanics, and signal processing to detect the building and infrastructure damage 5–11 . The others propose using the time‐varying characteristics of the short/long term data and their mathematical models to study the behavior and performance of structures under in‐service loads 12–17 . However, the mining of massive data has always been a difficult problem to solve.…”

Section: Introductionmentioning

confidence: 99%

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Digital modeling on the nonlinear mapping between multi‐source monitoring data of in‐service bridges

Zhao

Wei

et al. 2020

Struct Control Health Monit

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Nonlinear mapping of the fuzzy relation exists between structural inputs and outputs, as well as between structural global and local response. It is difficult for the numerical simulation to introduce the nonlinear effects of the variability of loading effects and the uneven deterioration of structure. The big monitoring data make it feasible to mine these nonlinear effects, and the network of deep learning is a good tool to establish the nonlinear mapping model between the multi-source monitoring data. Based on the temperature, strain, and dynamic displacement data from the structural health monitoring (SHM) system of an in-service bridge, the deep learning regression network of long short-term memory (LSTM) is designed and trained, which is used for modeling the nonlinear mapping between the structural input-output and the structural global-local response. The digital model of the nonlinear mapping of the temperature to temperature-induced strain, dynamic displacement to vehicleinduced strain, and vehicle-induced strain to dynamic displacement is established by the deep learning of LSTM regression network. The established nonlinear mapping model can be used to recover the abnormal and missing data with multi-source data, and it digitally linked the structural input-output and the structural global-local response. Then, the utilization of SHM data analytics will be enhanced, and the digital modeling for the in-service state of the bridge structure will be fast implemented.

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Section: Mapping Relation Between Multi‐source Bridge Monitoring Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital modeling on the nonlinear mapping between multi‐source monitoring data of in‐service bridges

Zhao

Wei

et al. 2020

Struct Control Health Monit

Self Cite

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“…High-speed trains produce strong dynamic effects on the bridge girders; and, conversely, the running stability of high-speed trains poses new requirements for the girder deformation of operating bridges [1]. With the continuous expansion of the high-speed railway network in China, a large number of bridges carrying multiple railway tracks are being put into operation.…”

Section: Introductionmentioning

confidence: 99%

“…Cross-section of the NDHRB deck showing different uses of the tracks 1. Nanjing Metro Line S3 Upstream (S3-U); 2 Beijing-Shanghai (BJ-SH) high-speed railway;3 Shanghai-Beijing (SH-BJ) high-speed railway;4 Shanghai-Wuhan-Chengdu (SH-WH-CD) high-speed railway;…”

mentioning

confidence: 99%

Ground-Based Radar Interferometry for Monitoring the Dynamic Performance of a Multitrack Steel Truss High-Speed Railway Bridge

et al. 2020

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With the continuous expansion of the high-speed railway network in China, long-span railway bridges carrying multiple tracks demand reliable and fast testing procedures and techniques. Bridge dynamic behavior analysis is a critical process in ensuring safe operation of structures. In this study, we present some experimental results of the vibration monitoring of a four-track high-speed railway bridge with a metro–track on each side: the Nanjing–Dashengguan high-speed railway bridge (NDHRB). The results were obtained using a terrestrial microwave radar interferometer named IBIS-S. The radar measurements were interpreted with the support of lidar point clouds. The results of the bridge dynamic response under different loading conditions, including high-speed trains, metro and wind were compared with the existing bridge structure health monitoring (SHM) system, underlining the high spatial (0.5 m) and temporal resolutions (50 Hz–200 Hz) of this technique for railway bridge dynamic monitoring. The detailed results can help engineers capturing the maximum train-induced bridge displacement. The bridge was also monitored by the radar from a lateral position with respect to the bridge longitudinal direction. This allowed us to have a more exhaustive description of the bridge dynamic behavior. The different effects induced by the passage of trains through different tracks and directions were distinguished. In addition, the space deformation map of the wide bridge deck under the eccentric load of trains, especially along the lateral direction (30 m), can help evaluating the running stability of high-speed trains.

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“…Considering the complicated nature of the actual temperature field in a bridge during operation, additional studies have attempted to analyze the thermal effects on bridge structures based on measured data and long-term monitoring data [4][5][6][7][8][9]. Furthermore, scholars have explored the behavior and performance of bridge structures under diurnal temperature loads via the validation of FE results using monitoring data [10][11][12]. Analysis of the temperature-induced behavior of bridge structures has solved a variety of engineering problems for many major infrastructure systems by employing such continuous studies.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal Displacement Behavior and Girder End Reliability of a Jointless Steel-Truss Arch Railway Bridge during Operation

Zhao

Nagarajaiah

2019

Applied Sciences

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The long length and complex service load form conflicts with the low limits of longitudinal and transverse displacements of jointless bridge design. The longitudinal displacements of the Nanjing Dashengguan Yangtze River Bridge, a jointless steel-truss arch railway bridge, and its girder end reliability are investigated in this article. The time–frequency characteristics of the longitudinal displacements of bearings and expansion joints are analyzed using the empirical wavelet transform. The long-term characteristics of the longitudinal displacements of bearings and expansion joints in the operation period are explored. Furthermore, the relative transverse displacements of the bridge girder end are calculated using longitudinal displacement monitoring data. The mechanical behaviors of the expansion device under relative transverse displacements are studied. The reliability of expansion devices and crossing trains under the effects of relative transverse displacements is studied using kernel density estimation. The main results demonstrate that: (1) The longitudinal displacements of bearings and expansion joints are mainly influenced by environmental temperature. (2) The maximum relative transverse displacement of the expansion joint is close to 1 mm in long-term bridge operation, with the transverse rail deflection at the expansion device approaching 1 mm, which reduces the stability of cross high-speed trains.

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Behavior Analysis and Early Warning of Girder Deflections of a Steel-Truss Arch Railway Bridge under the Effects of Temperature and Trains: Case Study

Cited by 67 publications

References 24 publications

Digital modeling on the nonlinear mapping between multi‐source monitoring data of in‐service bridges

Digital modeling on the nonlinear mapping between multi‐source monitoring data of in‐service bridges

Ground-Based Radar Interferometry for Monitoring the Dynamic Performance of a Multitrack Steel Truss High-Speed Railway Bridge

Longitudinal Displacement Behavior and Girder End Reliability of a Jointless Steel-Truss Arch Railway Bridge during Operation

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