Damage Detection of Self-Anchored Suspension Bridge Based on Neural Network Model and Genetic-Simulated Annealing Algorithm

Zhang, Qing Chen; Sun, Quan Sheng

doi:10.4028/www.scientific.net/amr.243-249.1963

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…Zhang and Sun proposed a damage detection and quantification method for self‐anchored suspension bridge using the BP neural network model and genetic‐simulated annealing algorithm; damage is detected using a BP neural network model, and the damage severity is assessed with the genetic‐simulated annealing algorithm; the global convergence effect of the proposed approach is enhanced compared with the conventional genetic algorithm. Wang and Ni proposed a damage detection and localization method for the suspension bridge; the proposed method is used for damage alarming based on the refined autoassociative neural network technique and damage localization based on the refined probabilistic neural network technique; a total of 15 damage cases in TsingMa suspension bridge located in Hongkong are considered in the numerical study to examine the performance of the method in which damage was introduced in bearings of the tower and deck, a side span cable and an anchorage, a tower saddle and a Tower cross‐beam, hangers, deck members, and rail way beams; the reliability of the proposed damage identification technique is verified: The autoassociative neural network using flexibility coefficients performs better than that formulated using modal frequencies in identifying minor damage with noisy data; results based on the adaptive probabilistic neural network are much better compared with the traditional probabilistic neural network in the case of high noise level.…”

Section: Recent Progress On Damage Identification Methods For Suspensmentioning

confidence: 99%

Recent progress and future trends on damage identification methods for bridge structures

Chatzi

Sim

et al. 2019

Struct Control Health Monit

233

112

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Summary Damage identification forms a key objective in structural health monitoring. Several state‐of‐the‐art review papers regarding progress in this field up to 2011 have been published. This paper summarizes the recent progress between 2011 and 2017 in the area of damage identification methods for bridge structures. This paper is organized based on the classification of bridge infrastructure in terms of fundamental structural systems, namely, beam bridges, truss bridges, arch bridges, cable‐stayed bridges, and suspension bridges. The overview includes theoretical developments, enhanced simulation attempts, laboratory‐scale implementations, full‐scale validation, and the summary for each type of bridges. Based on the offered review, some challenges, suggestions, and future trends in damage identification are proposed. The work can be served as a basis for both academics and practitioners, who seek to implement damage identification methods in next‐generation structural health monitoring systems.

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Section: Recent Progress On Damage Identification Methods For Suspensmentioning

confidence: 99%

Recent progress and future trends on damage identification methods for bridge structures

Chatzi

Sim

et al. 2019

Struct Control Health Monit

233

112

View full text Add to dashboard Cite

show abstract

“…2. Some studies propose two-step methods [100,102,108,115,122]; what is the necessity of implementing these methodologies?…”

Section: Discussionmentioning

confidence: 99%

A review of the application of the simulated annealing algorithm in structural health monitoring (1995-2021)

Ghannadi

Kourehli

Mirjalili

2023

Frattura Ed Integrità Strutturale

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In recent years, many innovative optimization algorithms have been developed. These algorithms have been employed to solve structural damage detection problems as an inverse solution. However, traditional optimization methods such as particle swarm optimization, simulated annealing (SA), and genetic algorithm are constantly employed to detect damages in the structures. This paper reviews the application of SA in different disciplines of structural health monitoring, such as damage detection, finite element model updating, optimal sensor placement, and system identification. The methodologies, objectives, and results of publications conducted between 1995 and 2021 are analyzed. This paper also provides an in-depth discussion of different open questions and research directions in this area.

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“…There are several damage identification methods for bridge structures currently available, including intelligent algorithm-based, Bayesian theory-based, time-domain signal processing-based damage identification methods, sparsity information and sparse recovery theory-based, neural network-based damage identification methods, and various modelbased methods [27][28][29][30][31][32][33]. These approaches are described conceptually and then proven in actual bridges through tests and experimentation.…”

Section: Discussionmentioning

confidence: 99%

Multi-Damage Identification of Multi-Span Bridges Based on Influence Lines

Zhang

Xie

et al. 2021

Coatings

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The framework security of a bridge is essential as a critical component of traffic engineering. Even though the bridge structure is damaged to various degrees due to various reasons, the bridge will be wrecked when the damage reaches a particular level, suggesting a negative influence on people’s lives. Based on the current situation and existing problems of structural damage identification of bridges, a structural damage identification technology of continuous beam bridges based on deflection influence lines is proposed in this paper in order to keep track of and always detect broken bridge elements, thereby extending the bridge’s service life and reducing the risk of catastrophic accidents. The line function expression of deflection impact on a multi-span continuous beam bridge was first obtained using Graphic Multiplication theory. From the theoretical level, the influence line function of the continuous beam bridge without extensive damage was computed, and a graph was generated. The photographs of the DIL as well as the first and second derivatives, the deflection influence line distinction and its first and second derivatives, and the DIL distinction and its first and second derivatives of a continuous beam bridge in a single position and multi-position destruction were fitted in this paper. Finally, after comparing multiple work conditions and multiple measuring points, it was found that the first derivative of deflection influence line difference had the best damage identification effect. The design was completed and tested, which had verified the feasibility of this theory.

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Damage Detection of Self-Anchored Suspension Bridge Based on Neural Network Model and Genetic-Simulated Annealing Algorithm

Cited by 5 publications

References 3 publications

Recent progress and future trends on damage identification methods for bridge structures

Recent progress and future trends on damage identification methods for bridge structures

A review of the application of the simulated annealing algorithm in structural health monitoring (1995-2021)

Multi-Damage Identification of Multi-Span Bridges Based on Influence Lines

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