Abstract
The paper presents a study about the defect detection on structural elements of existing reinforced concrete bridges through a machine-learning approach. In detail, the proposed methodology aims to explore the possibility of automatically recognising deficiencies on bridges’ elements, e.g., cracks, humidity, by employing a training of existing convolutional neural networks on a set of photos. The initial database, characterized by 2.436 images, has been firstly selected and after has been classified by domain experts according to the requirements of the new Italian guidelines on structural safety of existing bridges. The results show a good effectiveness and accuracy of the proposed methodology, opening new scenarios for the automatic defect detection on bridges, mainly aimed to support management companies surveyors in the phase of in-situ structural inspection.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ministero delle Infrastrutture e dei Trasporti. Linee Guida per la Classificazione e Gestione del Rischio, la Valutazione della Sicurezza ed il Monitoraggio dei Ponti Esistenti (2020). (in Italian)
Xie, Y., Ebad Sichani, M., Padgett, J.E., DesRoches, R.: The promise of implementing machine learning in earthquake engineering: a state-of-the-art review. Earthq. Spectra 36(4), 1769–1801 (2020). https://doi.org/10.1177/8755293020919419
Sun, H., Burton, H.V., Huang, H.: Machine learning applications for building structural design and performance assessment: state-of-the-art review. J. Build. Eng. 33, 101816 (2020). https://doi.org/10.1016/j.jobe.2020.101816
Ruggieri, S., Cardellicchio, A., Leggieri, V., Uva, G.: Machine-learning based vulnerability analysis of existing buildings. Autom. Constr. 132, 103936 (2021). https://doi.org/10.1016/j.autcon.2021.103936
Cardellicchio, A., Ruggieri, S., Leggieri, V., Uva, G.: View VULMA: data set for training a machine-learning tool for a fast vulnerability analysis of existing buildings. Data. 7(1), 4 (2022). https://doi.org/10.3390/data7010004
Cha, Y.J., Choi, W., Büyüköztürk, O.: Deep learning-based crack damage detection using convolutional neural networks. Comput. Civ. Infrastruct. Eng. 32, 361–378 (2017). https://doi.org/10.1111/mice.12263
Cha, Y.J., Choi, W., Suh, G., Mahmoudkhani, S., Büyüköztürk, O.: Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Comput. Civ. Infrastruct. Eng. 33, 731–47 (2018). https://doi.org/10.1111/mice.12334
Zhu, J., Zhang, C., Qi, H., Lu, Z.: Vision-based defects detection for bridges using transfer learning and convolutional neural networks. Struct. Infrastruct. Eng. 16(7), 1037–1049 (2020). https://doi.org/10.1080/15732479.2019.1680709
Zhang, A., et al.: Automated pixel-level pavement crack detection on 3D asphalt surfaces using a deep-learning network. Comput. Aided Civ. Infrastruct. Eng. 32(10), 805–819 (2017). https://doi.org/10.1111/mice.12297
Yang, X., Li, H., Yu, Y., Luo, X., Huang, T., Yang, X.: Automatic pixel-level crack detection and measurement using fully convolutional network. Comput. Aided Civ. Infrastruct. Eng. 33(12), 1090–1109 (2018). https://doi.org/10.1111/mice.12412
Yang, L., Li, B., Li, W., Liu, Z., Yang, G., Xiao, J.: Deep concrete inspection using unmanned aerial vehicle towards CSSC database. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 24–28, September 2017
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28, 91–99 (2015)
Kim, I., Jeon, H., Baek, S., Hong, W., Jung, H.: Application of crack identification techniques for an aging concrete bridge inspection using an unmanned aerial vehicle. Sensors 18(6), 1881 (2018). https://doi.org/10.3390/s18061881
Li, R., Yuan, Y., Zhang, W., Yuan, Y.: Unified vision-based methodology for simultaneous concrete defect detection and geolocalization. Comput. Aided Civ. Infrastruct. Eng. 33(7), 527–544 (2018). https://doi.org/10.1111/mice.12351
Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part I. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016). https://doi.org/10.1109/CVPR.2016.91
Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., Omata, H.: Road damage detection and classification using deep neural networks with smartphone images. Comput. Aided Civ. Infrastruct. Eng. 33(12), 1127–1141 (2018). https://doi.org/10.1111/mice.12387
https://github.com/ultralytics/yolov5. Accessed 23 Mar 2022
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Cardellicchio, A., Ruggieri, S., Nettis, A., Patruno, C., Uva, G., Renò, V. (2022). Deep Learning Approaches for Image-Based Detection and Classification of Structural Defects in Bridges. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds) Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13373. Springer, Cham. https://doi.org/10.1007/978-3-031-13321-3_24
Download citation
DOI: https://doi.org/10.1007/978-3-031-13321-3_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-13320-6
Online ISBN: 978-3-031-13321-3
eBook Packages: Computer ScienceComputer Science (R0)