Damage diagnosis in bridge structures using rotation influence line: Validation on a cable-stayed bridge

Alamdari, Mehrisadat Makki; Kildashti, Kamyar; Samali, Bijan; Goudarzi, Hamid Valipour

doi:10.1016/j.engstruct.2019.01.124

Cited by 74 publications

(40 citation statements)

References 40 publications

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“…It can be used for the classification. Obtaining the maximum classification interval is equivalent to the minimum value of 1 2 • ω 2 . For linear and inseparable data, the relaxation variable ξ i and the penalty coefficient c are introduced.…”

Section: A the Support Vector Machine Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Particle Swarm Optimization-Based SVM for Classification of Cable Surface Defects of the Cable-Stayed Bridges

Guo

2020

IEEE Access

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The machine vision system was employed to inspect the surface defects of bridge cables of cable-stayed bridges. After the acquisition and preprocessing of the defect images, it is necessary to classify and identify the defects of the cables to meet the requirements of non-destructive testing and evaluation. In this paper, feature extraction for defect images was performed using mathematical statistical methods. After that, 10 feature parameters including shape features, grayscale features and texture features of the defect images were obtained and selected for a classification model of support vector machine (SVM). To improve the SVM classification performance, the particle swarm optimization algorithm (PSO) was adopted to obtain the punish factor c and the kernel parameter g of the SVM model, namely the PSO-SVM algorithm. Finally, our PSO-SVM classification model was employed to implement the classification of real surface defect images of the bridge cables. Longitudinal crack, transverse crack, surface corrosion, and pothole defect can be automatically identified and the classification accuracy reached 96.25%. The experimental results showed that the PSO-SVM model can improve the classification performance of the surface defects. Based on the effective classification, we can find the distribution characteristics of the surface defects of the cable. It is very important to analyze the relationship between the type of surface defects and the material of the protective layer, so as to adopt appropriate materials and reasonable maintenance measures. Thus, it has a great significance in the structural health monitoring of bridge cables.

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Section: A the Support Vector Machine Algorithmmentioning

confidence: 99%

“…Long-span and super long-span cable-stayed bridges and suspension bridges have been widely used in recent years for the traffic construction [1], [2]. Cables are the main components of such bridges and directly affect the safety [3].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization-Based SVM for Classification of Cable Surface Defects of the Cable-Stayed Bridges

Guo

2020

IEEE Access

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“…A study into identifying simulated cable stiffness loss in a cable stayed bridge using rotation measurements concluded that only two measurement points were adequate to monitor the integrity of the bridge structure using rotation based measurement [3].…”

Section: Introductionmentioning

confidence: 99%

Identifying damage in a bridge by analysing rotation response to a moving load

McGeown¹,

Huseynov²,

Hester³

et al. 2021

Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations

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A recent survey of Europe's highway infrastructure has concluded that almost half of Europe's bridges are nearing the end of their design live. Work in the wider Structural Health Monitoring sector is aiming to develop reliable and cost-effective methods for verifying condition, remaining service life and safety of ageing structures. Most bridge condition assessment methods are based on deflection, acceleration or strain measurements. This paper looks at the possibility of using rotation measurements as a main parameter to identify damage. This study looks at numerical analyses of a moving point load on a one-dimensional bridge model to provide the theoretical basis of the proposed damage detection method. It is shown that when local damage occurs, even when it is remote from a sensor location, it results in an increase in the magnitude of rotation measurements. This study looks at how best to exploit this fact for damage detection. In the study a number of damage scenarios, sensor locations, and load arrangements are investigated, and their influence on the ability of the algorithm to detect damage are reported.

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“…At the same time, it is worth noting that in recent years, a damage identification method based on influence line changes has emerged . The definition of the influence line is shown as follows: When the unit concentrated load moves through the bridge in a perpendicular direction, the response curve for a certain point in a certain section is called influence line.…”

Section: Introductionmentioning

confidence: 99%

“…Moses firstly applies the bridge influence line in bridge‐weight‐in motion system. B‐WIM system can measure the axle weight of heavy trucks by using bridge static responses and bridge influence line . The accuracy of the bridge influence line will influence the precision of the measured axle weight.…”

Section: Introductionmentioning

confidence: 99%

Bridge influence line identification from structural dynamic responses induced by a high‐speed vehicle

Yang

et al. 2020

Struct Control Health Monit

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Summary The bridge influence line can reflect the structural behavior under moving vehicle loads, which is becoming a proposing tool to study the bridge behavior during service life. A high‐speed vehicle will induce dynamic effect on the bridge, which will significantly change the shape of response curve. It is essential to consider the dynamic response for the accurate identification of the bridge influence line. This paper firstly summarizes current research activities for bridge influence line identification and components of the bridge dynamic responses. A dynamic fluctuation part elimination method based on empirical mode decomposition is proposed, based on which the bridge static influence line is identified by static bridge influence line identification method. Finally, a vehicle–bridge coupling model of a simply supported beam excited by passing vehicles with three different speeds is introduced to illustrate the effectiveness of the proposed identification method. The result of this study provides an effective identification method of the bridge static influence line, which can lead to a satisfactory identification results based on the dynamic responses induced by high‐speed vehicle loads.

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Damage diagnosis in bridge structures using rotation influence line: Validation on a cable-stayed bridge

Cited by 74 publications

References 40 publications

Particle Swarm Optimization-Based SVM for Classification of Cable Surface Defects of the Cable-Stayed Bridges

Particle Swarm Optimization-Based SVM for Classification of Cable Surface Defects of the Cable-Stayed Bridges

Identifying damage in a bridge by analysing rotation response to a moving load

Bridge influence line identification from structural dynamic responses induced by a high‐speed vehicle

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