Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection

Li, Sheng; Zuo, Xiang; Li, Zhengying; Wang, Honghai

doi:10.3390/s20030911

Open AccessArticle

Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection

¹

National Engineering Laboratory for Fiber Optic Sensing Technology, Wuhan University of Technology, Wuhan 430070, China

²

School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China

^*

Author to whom correspondence should be addressed.

Sensors 2020, 20(3), 911; https://doi.org/10.3390/s20030911

Submission received: 4 January 2020 / Revised: 5 February 2020 / Accepted: 6 February 2020 / Published: 8 February 2020

(This article belongs to the Special Issue Optical Sensors for Structural Health Monitoring)

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Abstract

Improving the accuracy and efficiency of bridge structure damage detection is one of the main challenges in engineering practice. This paper aims to address this issue by monitoring the continuous bridge deflection based on the fiber optic gyroscope and applying the deep-learning algorithm to perform structural damage detection. With a scale-down bridge model, three types of damage scenarios and an intact benchmark were simulated. A supervised learning model based on the deep convolutional neural networks was proposed. After the training process under ten-fold cross-validation, the model accuracy can reach 96.9% and significantly outperform that of other four traditional machine learning methods (random forest, support vector machine, k-nearest neighbor, and decision tree) used for comparison. Further, the proposed model illustrated its decent ability in distinguishing damage from structurally symmetrical locations.

Keywords: bridge damage detection; fiber optic gyroscope; deep learning; convolutional neural network

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MDPI and ACS Style

Li, S.; Zuo, X.; Li, Z.; Wang, H. Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection. Sensors 2020, 20, 911. https://doi.org/10.3390/s20030911

AMA Style

Li S, Zuo X, Li Z, Wang H. Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection. Sensors. 2020; 20(3):911. https://doi.org/10.3390/s20030911

Chicago/Turabian Style

Li, Sheng, Xiang Zuo, Zhengying Li, and Honghai Wang. 2020. "Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection" Sensors 20, no. 3: 911. https://doi.org/10.3390/s20030911

APA Style

Li, S., Zuo, X., Li, Z., & Wang, H. (2020). Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection. Sensors, 20(3), 911. https://doi.org/10.3390/s20030911

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Applying Deep Learning to Continuous Bridge Deflection Detected by Fiber Optic Gyroscope for Damage Detection

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