Benchmark for Evaluating Performance in Visual Inspection of Fatigue Cracking in Steel Bridges

Campbell, Leslie E.; Connor, Robert; Whitehead, Julie; Washer, Glenn

doi:10.1061/(asce)be.1943-5592.0001507

Cited by 41 publications

(30 citation statements)

References 5 publications

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“…The accuracy of visual inspection has been researched by Campbell et al (2019). They had invited a diverse group of 30 inspectors to complete a full day, hands-on inspection of 147 steel bridge specimens to establish a quantitative measure of Fig.…”

Section: Posterior Probabilitiesmentioning

confidence: 99%

Risk Analysis for Decision Support — A Case Study on Fatigue Assessment of a Steel Bridge

Wang¹,

Leander²,

Karoumi³

2020

Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conf

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Fatigue is among the most critical forms of deterioration damage that occurs to steel bridges. It causes a decline of the safety level of bridges over time. Much research has been accomplished on a reliability level, which focuses on evaluating the service life of the selected detail or the whole structure. However, the decisions aiming to keep the existing bridges in service should be further considered, i.e. whether interventions are needed, what kind of interventions could be applied, which intervention is the optimal choice to be both sustainable and economical within a budget restraint considered. Therefore a risk-based decision making framework is necessary for bridges service life extension. In this paper, a risk-based decision support framework is formulated aiming for rational decisions, which is presented as a decision tree. It presents possible intervention actions, evaluates the corresponding probability of failure and integrates them with possible consequences. A case study of the Rautasjokk Bridge in Sweden is incorporated to demonstrate the framework. A separation of the load bearing structure in subsystems is suggested for risk analysis and decision optimization. The expected result and how to prioritize the calculated utilities are discussed. And the results are compared with the reliability analysis to check if they are consistent.

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Section: Posterior Probabilitiesmentioning

confidence: 99%

Risk Analysis for Decision Support — A Case Study on Fatigue Assessment of a Steel Bridge

Wang¹,

Leander²,

Karoumi³

2020

Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conf

View full text Add to dashboard Cite

show abstract

“…Bridge owners and bridge management departments put large amounts of effort and resources into the visual inspection task, guideline, and personnel [ 5 ]. A qualified inspector needs to go through the qualification, training, and certification of inspectors [ 6 ]. However, the accuracies of visual inspection highly rely on the inspectors’ subjective judgments.…”

Section: Introductionmentioning

confidence: 99%

“…However, the accuracies of visual inspection highly rely on the inspectors’ subjective judgments. According to Campbell et al’s recent study [ 6 ], the experience and training of inspectors, the field temperature during visual inspection, and the inspection duration were significantly correlated with the detection rate. The objectives of the visual inspection items in their study were exactly fatigue cracks in steel bridges.…”

Section: Introductionmentioning

confidence: 99%

Pixel-Level Fatigue Crack Segmentation in Large-Scale Images of Steel Structures Using an Encoder–Decoder Network

Dong

Jin

et al. 2021

Sensors

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Fatigue cracks are critical types of damage in steel structures due to repeated loads and distortion effects. Fatigue crack growth may lead to further structural failure and even induce collapse. Efficient and timely fatigue crack detection and segmentation can support condition assessment, asset maintenance, and management of existing structures and prevent the early permit post and improve life cycles. In current research and engineering practices, visual inspection is the most widely implemented approach for fatigue crack inspection. However, the inspection accuracy of this method highly relies on the subjective judgment of the inspectors. Furthermore, it needs large amounts of cost, time, and labor force. Non-destructive testing methods can provide accurate detection results, but the cost is very high. To overcome the limitations of current fatigue crack detection methods, this study presents a pixel-level fatigue crack segmentation framework for large-scale images with complicated backgrounds taken from steel structures by using an encoder-decoder network, which is modified from the U-net structure. To effectively train and test the images with large resolutions such as 4928 × 3264 pixels or larger, the large images were cropped into small images for training and testing. The final segmentation results of the original images are obtained by assembling the segment results in the small images. Additionally, image post-processing including opening and closing operations were implemented to reduce the noises in the segmentation maps. The proposed method achieved an acceptable accuracy of automatic fatigue crack segmentation in terms of average intersection over union (mIOU). A comparative study with an FCN model that implements ResNet34 as backbone indicates that the proposed method using U-net could give better fatigue crack segmentation performance with fewer training epochs and simpler model structure. Furthermore, this study also provides helpful considerations and recommendations for researchers and practitioners in civil infrastructure engineering to apply image-based fatigue crack detection.

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“…Early stage assessment is often restricted to visual inspections [14][15][16] that are subjective and not always reliable, or calculations of structural capacity [17], which are very conservative with the unrealistic assumptions and models related to loading and material properties. The assessment of RC decks requires methods that can give both the structural characteristics and response of RC structural elements under traffic and environmental loading.…”

Section: Introductionmentioning

confidence: 99%

Structural condition assessment of reinforced-concrete bridges based on acoustic emission and strain measurements

Bayane,

Brühwiler

2020

J Civil Struct Health Monit

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A reliable novel monitoring approach is developed to assess the structural condition of reinforced-concrete bridge elements. The approach is based on combining acoustic emission technique and strain gauge measurements, and it is illustrated by a case study of a composite steel–concrete viaduct in service since 1957. Monitoring was performed on its reinforced-concrete deck slab under traffic and environmental loading for one year. The monitoring setup and procedure are presented. The variation of acoustic emission signals is evaluated regarding strain and temperature measurements. Parametric study, pencil-break test, statistical analysis, crack classification and b-value analysis are performed to assess the structural condition. The acoustic emission activity of the reinforced-concrete slab is evaluated for 1 year under operational conditions. From the monitoring approach, it was possible to identify the nature of the cracking activity in the concrete slab as a function of traffic loading and temperature and to assess in time the condition of the slab-girder connection.

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Benchmark for Evaluating Performance in Visual Inspection of Fatigue Cracking in Steel Bridges

Cited by 41 publications

References 5 publications

Risk Analysis for Decision Support — A Case Study on Fatigue Assessment of a Steel Bridge

Risk Analysis for Decision Support — A Case Study on Fatigue Assessment of a Steel Bridge

Pixel-Level Fatigue Crack Segmentation in Large-Scale Images of Steel Structures Using an Encoder–Decoder Network

Structural condition assessment of reinforced-concrete bridges based on acoustic emission and strain measurements

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