Serious Damage Localization of Continuous Girder Bridge by Support Reaction Influence Lines

Cheng, Qifeng; Ruan, Xuzhi; Wang, Yi–Ze; Chen, Zhiwei

doi:10.3390/buildings12020182

Cited by 4 publications

(3 citation statements)

References 34 publications

(39 reference statements)

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“…When a crack occurs in the structure of a beam, some structural properties will change. A crack in a beam can be identified by the curvature mode shape of the beam, as presented by Pandey et al [2]. The authors now introduce a verified relationship between the flexural stiffness and amplitude of curvature of natural modes of vibration at a point in or on a common beam, which could be used to identify a crack effectively.…”

Section: A Previous Crack Identification Methodsmentioning

confidence: 95%

“…Methods for identifying damage in structures, such as aircrafts, bridges, ships, and buildings is an important area of research and development that has a great potential for cost-saving and safety improvement to those structures [1][2][3]. The presence of cracks in a structure brings about local variations in the stiffness of the structure.…”

Section: Introductionmentioning

confidence: 99%

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Crack Identification in Cantilever Beam under Moving Load Using Change in Curvature Shapes

et al. 2022

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Cracks in structural components may ultimately lead to failure of the structure if not identified sufficiently early. This paper presents a crack-identification method based on time-domain. Captured time-domain data are processed into central difference approximation of displacement of each node (point) in the structure. Abnormally high central difference approximation of displacement of a node relative to those of its neighborhood points indicates a crack at that point. A suite of simulation experiments and numerical calculations was conducted to find out whether the proposed identification method could accurately identify the location of a crack in a cantilever beam under moving load compared to the location found by an exact solution method, and the outcomes indicated that it was as able as the analytical method. The proposed method is an FEA analysis, an approach familiar to virtually every engineer. Therefore, the relative amount of time and effort spent on developing the proposed method for a specific application is much less than those spent on developing an analytical method. The saved time and effort should enable more engineering personnel to perform routine checks on structural elements of their interest more simply and frequently.

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Section: A Previous Crack Identification Methodsmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Crack Identification in Cantilever Beam under Moving Load Using Change in Curvature Shapes

et al. 2022

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show abstract

“…The signal has been frequently disrupted by noise during the field test and recording. Gaussian white noise with signal-to-noise ratio magnitudes of about 33 and 53 dB has been applied to the acceleration response signals to guarantee that the trained numerical model reflects the same characteristics as the real field test [30]. At a speed of 30 km/h, the vehicle with a front axle load of 1560 kg and a rear axle load of 1820 kg passed through the arch bridge.…”

Section: Calculation Of Data Noisementioning

confidence: 99%

Structural Damage Identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach

Xiong¹,

Liang²,

She³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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A huge number of old arch bridges located in rural regions are at the peak of maintenance. The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge, owing to the absence of technical resources and sufficient funds in rural regions. There is an urgent need for an economical, fast, and accurate damage identification solution. The authors proposed a damage identification system of an old arch bridge implemented with a machine learning algorithm, which took the vehicle-induced response as the excitation. A damage index was defined based on wavelet packet theory, and a machine learning sample database collecting the denoised response was constructed. Through comparing three machine learning algorithms: Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (R.F.), the R.F. damage identification model were found to have a better recognition ability. Finally, the Particle Swarm Optimization (PSO) algorithm was used to optimize the number of subtrees and split features of the R.F. model. The PSO optimized R.F. model was capable of the identification of different damage levels of old arch bridges with sensitive damage index. The proposed framework is practical and promising for the old bridge's structural damage identification in rural regions.

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Integration of multi-point influence line information for damage localization of bridge structures

Zhang,

Zhu,

Zhou

2023

J Civil Struct Health Monit

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Serious Damage Localization of Continuous Girder Bridge by Support Reaction Influence Lines

Cited by 4 publications

References 34 publications

Crack Identification in Cantilever Beam under Moving Load Using Change in Curvature Shapes

Crack Identification in Cantilever Beam under Moving Load Using Change in Curvature Shapes

Structural Damage Identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach

Integration of multi-point influence line information for damage localization of bridge structures

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