Damage Detection of Structures Subject to Nonlinear Effects of Changing Environmental Conditions

Wah, William Soo Lon; Chen, Yung-Tsang; Roberts, Gethin Wyn; Elamin, Ahmed

doi:10.1016/j.proeng.2017.04.481

Cited by 17 publications

(10 citation statements)

References 7 publications

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“…The features of PCA are also useful for pattern recognition of high-dimensional data using dimension reduction and there are many studies that use PCA for SHM (e.g., [12,13,14,15,16]). Manson [12] developed a feature-based damage detection technique using PCA components that can detect damage while filtering out temperature effects, which have a significant impact on the structure.…”

Section: Related Studiesmentioning

confidence: 99%

“…The correlation between PCA components and structural damage was clarified, and PCA obtained from the signal data of the structure showed that anomalies can be used for damage detection. Wah et al [16] developed a damage detection technique that uses a Gaussian mixture model in PCA and applied it to truss bridges to verify the technique.…”

Section: Related Studiesmentioning

confidence: 99%

See 1 more Smart Citation

A Novelty Detection Approach for Tendons of Prestressed Concrete Bridges Based on a Convolutional Autoencoder and Acceleration Data

Lee

Jeong

Sim

et al. 2019

Sensors

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The most important structural element of prestressed concrete (PSC) bridges is the prestressed tendon, and in order to ensure safety of such bridges, it is very important to determine whether the tendon is damaged. However, it is not easy to detect tendon damage in real time. This study proposes a novelty detection approach for damage to the tendons of PSC bridges based on a convolutional autoencoder (CAE). The proposed method employs simulation data from nine accelerometers. The accuracies of CAEs for multi-vehicle are 79.5%–85.8% for 100% and 75% damage severities with all error levels and 50% damage severity without error. However, the accuracies for 50% damage severity with 5% and 10% error levels drop to 69.4%–73.3%. The accuracies of CAEs for single-vehicle ranges from 90.1%–95.1% for all damage severities and error levels that are satisfactory. The findings indicate that the CAE approach for multi-vehicle can be effective when the damages are severe, but not when moderate. Meanwhile, if acceleration data can be obtained for single-vehicle, then the CAE approach can provide a highly accurate and robust method of tendon damage detection in PSC bridges in use, even if the measurement errors are significant.

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Section: Related Studiesmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

A Novelty Detection Approach for Tendons of Prestressed Concrete Bridges Based on a Convolutional Autoencoder and Acceleration Data

Lee

Jeong

Sim

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Then the windowed data set is processed using PCA method as mentioned above. Therefore, the windowed features of eigenvectors and the corresponding eigenvalues are obtained, which are time‐varying features utilized in long‐term SHM to assess the status of the structure …”

Section: Methodsmentioning

confidence: 99%

Bridge condition monitoring using fixed moving principal component analysis

Nie

Guo

et al. 2020

Struct Control Health Monit

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Summary This paper proposes a data‐driven damage detection method based on fixed moving principal component analysis (FMPCA) to analyze structural dynamic responses and monitor the bridge operational condition and the damage occurrence. The damage indices based on principal components (PCs) and eigenvalues can be calculated continuously by applying a fixed moving window. The length of the moving window is determined by using a new criterion based on the convergent spectrum of cumulative contribution ratio. Numerical simulations and experimental tests in the laboratory on beam bridge models subjected to stochastic loads are conducted to investigate the accuracy and effectiveness of the proposed approach. Both simulation and experimental results indicate that using the FMPCA can well analyze the dynamic vibration data to detect damage or abnormal vibration behavior during the operational condition. It can be used to accurately monitor the time instant of damage occurrence, which is very important in long‐term monitoring of civil engineering structures. The proposed method is successfully applied to analyze the data recorded during an incident that a real large‐scale suspension bridge was slightly scraped by the mast of a sand ship, which further verifies the effectiveness and feasibility of this method in engineering applications. The results also indicate that the bridge was not damaged after the incident but presented a short time abnormal vibration behavior owing to the impact of the ship mast.

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“…In many cases, temperature variation can have a greater effect on the dynamic behaviour of the bridge than the presence of damage. This can result in false damage identification, or—in some cases—can obscure the detection of real damage [ 24 , 25 ]. The effect can be reduced through data normalisation and statistical training methods, such as principal component analysis [ 26 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Bridge Damage Detection Approach Using a Roving Camera Technique

Lydon

Kromanis

et al. 2021

Sensors

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Increasing extreme climate events, intensifying traffic patterns and long-term underinvestment have led to the escalated deterioration of bridges within our road and rail transport networks. Structural Health Monitoring (SHM) systems provide a means of objectively capturing and quantifying deterioration under operational conditions. Computer vision technology has gained considerable attention in the field of SHM due to its ability to obtain displacement data using non-contact methods at long distances. Additionally, it provides a low cost, rapid instrumentation solution with low interference to the normal operation of structures. However, even in the case of a medium span bridge, the need for many cameras to capture the global response can be cost-prohibitive. This research proposes a roving camera technique to capture a complete derivation of the response of a laboratory model bridge under live loading, in order to identify bridge damage. Displacement is identified as a suitable damage indicator, and two methods are used to assess the magnitude of the change in global displacement under changing boundary conditions in the laboratory bridge model. From this study, it is established that either approach could detect damage in the simulation model, providing an SHM solution that negates the requirement for complex sensor installations.

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Damage Detection of Structures Subject to Nonlinear Effects of Changing Environmental Conditions

Cited by 17 publications

References 7 publications

A Novelty Detection Approach for Tendons of Prestressed Concrete Bridges Based on a Convolutional Autoencoder and Acceleration Data

A Novelty Detection Approach for Tendons of Prestressed Concrete Bridges Based on a Convolutional Autoencoder and Acceleration Data

Bridge condition monitoring using fixed moving principal component analysis

Bridge Damage Detection Approach Using a Roving Camera Technique

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