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Two-step approach for fatigue crack detection in steel bridges using convolutional neural networks

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Abstract

The advent of parallel computing capabilities, further boosted through the exploitation of graphics processing units, has resulted in the surge of new, previously infeasible, algorithmic schemes for structural health monitoring (SHM) tasks, such as the use of convolutional neural networks (CNNs) for vision-based SHM. This work proposes a novel approach for crack recognition in digital images based on coupling of CNNs and suited image processing techniques. The proposed method is applied on a dataset comprising images of the welding joints of a long-span steel bridge, collected via high-resolution consumer-grade digital cameras. The studied dataset includes photos taken in sub-optimal light and exposure conditions, with several noise contamination sources such as handwriting scripts, varying material textures, and, in some cases, under presence of external objects. The reference pixels representing the cracks, together with the crack width and length, are available and used for training and validating the proposed model. Although the proposed framework requires some knowledge of the “damaged areas”, it alleviates the need for precise labeling of the cracks in the training dataset. Validation of the model by means of application on an unlabeled image set reveals promising results in terms of accuracy and robustness to noise sources.

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Availability of data and materials

The data supporting the results reported in this paper have been provided by the organizers of the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020).

Code availability

Code is available upon motivated request.

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Acknowledgements

The authors would like to kindly acknowledge the organizers of the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), ANCRiSST, Harbin Institute of Technology (China), and University of Illinois at Urbana-Champaign (USA) for their generously providing the invaluable data from actual structures. The authors also would like to thank the chairs of IPC-SHM 2020 Prof. Hui Li, and Prof. Billie F. Spencer Jr for their leadership on the competition. Research described in this paper was financially supported by the Real-time Earthquake Risk Reduction for a Resilient Europe ‘RISE’ project, financed under the European Union’s Horizon 2020 research and innovation programme, under grant agreement No 821115, by the ETH Grant (ETH-11 18-1) Dynarisk—“Enabling Dynamic Earthquake Risk Assessment”, as well as by the Singapore-ETH center (SEC) under contract no. FI 370074011-370074016.

Funding

Research described in this paper was financially supported by the Real-time Earthquake Risk Reduction for a Resilient Europe ‘RISE’ project, financed under the European Union’s Horizon 2020 research and innovation programme, under grant agreement No 821115, by the ETH Grant (ETH-11 18-1) Dynarisk—“Enabling Dynamic Earthquake Risk Assessment”, as well as by the Singapore-ETH center (SEC) under contract no. FI 370074011-370074016.

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Authors and Affiliations

  1. Department of Civil, Chemical, Environmental, and Materials Engineering, University of Bologna, Viale del Risorgimento 2, 40136, Bologna, Italy

    Said Quqa

  2. Department of Civil, Environmental and Geomatic Engineering, ETH Zurich, Stefano-Franscini-Platz 5, 8093, Zurich, Switzerland

    Panagiotis Martakis, Yves Reuland & Eleni Chatzi

  3. School of Engineering, Institute for Infrastructure and Environment, University of Edinburgh, Alexander Graham Bell Building, Thomas Bayes Road, Edinburgh, EH9 3FG, UK

    Artur Movsessian

  4. Singapore-​ETH Centre, 1 Create Way, CREATE Tower, 138602, Singapore, Singapore

    Sai Pai

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  1. Said Quqa
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Correspondence to Said Quqa.

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Quqa, S., Martakis, P., Movsessian, A. et al. Two-step approach for fatigue crack detection in steel bridges using convolutional neural networks. J Civil Struct Health Monit 12, 127–140 (2022). https://doi.org/10.1007/s13349-021-00537-1

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