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A Damage Localization Approach for Rahmen Bridge Based on Convolutional Neural Network

  • Construction Management
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KSCE Journal of Civil Engineering Aims and scope Submit manuscript

Abstract

Damage localization is the process of detecting the location of damage using a structural health monitoring system. However, existing damage localization methods cannot be used for localizing the damage of bridges in real time because of their slow testing speed. Thus, in this study a damage localization approach was developed using a convolutional neural network (CNN). To develop the CNN model for damage localization, simulation data was generated through a numerical model of a reinforced concrete Rahmen bridge with static loading conditions. The proposed CNN-based approach aims to identify 12 single damage locations or no damage. The approach was trained and tested with three different data set generated with three damage severities, and it was possible to estimate the damage location with an accuracy of 87.3% when the damage severity in the bridge is serious. The results showed that the deep learning has the potential to overcome the limitations of existing damage localization techniques.

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Acknowledgements

This research was supported by a grant (18CTAP-C117271-03) from Technology Advancement Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.

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Authors and Affiliations

  1. Dept. of Civil Engineering, Inha University, Incheon, 22212, Korea

    Kanghyeok Lee & Do Hyoung Shin

  2. Dept. of Civil, Environmental and Architectural Engineering, Korea University, Seoul, 02841, Korea

    Namju Byun

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  1. Kanghyeok Lee
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  2. Namju Byun
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Correspondence to Do Hyoung Shin.

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Lee, K., Byun, N. & Shin, D.H. A Damage Localization Approach for Rahmen Bridge Based on Convolutional Neural Network. KSCE J Civ Eng 24, 1–9 (2020). https://doi.org/10.1007/s12205-020-0707-9

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  • DOI: https://doi.org/10.1007/s12205-020-0707-9

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