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Communication

Application of C-LSTM Networks to Automatic Labeling of Vehicle Dynamic Response Data for Bridges

1
Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Ibaraki, Japan
2
Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Ibaraki, Japan
3
Center for Artificial Intelligence Research, University of Tsukuba, 1-1-1 Tennodai, Tsukuba 305-8577, Ibaraki, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Len Gelman and Sio Iong Ao
Sensors 2022, 22(9), 3486; https://doi.org/10.3390/s22093486
Received: 15 February 2022 / Revised: 25 April 2022 / Accepted: 28 April 2022 / Published: 3 May 2022
Maintaining bridges that support road infrastructure is critical to the economy and human life. Structural health monitoring of bridges using vibration includes direct monitoring and drive-by monitoring. Drive-by monitoring uses a vehicle equipped with accelerometers to drive over bridges and estimates the bridge’s health from the vehicle vibration obtained. In this study, we attempt to identify the driving segments on bridges in the vehicle vibration data for the practical application of drive-by monitoring. We developed an in-vehicle sensor system that can measure three-dimensional behavior, and we propose a new problem of identifying the driving segment of vehicle vibration on a bridge from data measured in a field experiment. The “on a bridge” label was assigned based on the peaks in the vehicle vibration when running at joints. A supervised binary classification model using C-LSTM (Convolution—Long-Term Short Memory) networks was constructed and applied to data measured, and the model was successfully constructed with high accuracy. The challenge is to build a model that can be applied to bridges where joints do not exist. Therefore, future work is needed to propose a running label on bridges based on bridge vibration and extend the model to a multi-class model. View Full-Text
Keywords: drive-by bridge monitoring; vehicle bridge interaction; neural network; C-LSTM; field test drive-by bridge monitoring; vehicle bridge interaction; neural network; C-LSTM; field test
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MDPI and ACS Style

Shin, R.; Okada, Y.; Yamamoto, K. Application of C-LSTM Networks to Automatic Labeling of Vehicle Dynamic Response Data for Bridges. Sensors 2022, 22, 3486. https://doi.org/10.3390/s22093486

AMA Style

Shin R, Okada Y, Yamamoto K. Application of C-LSTM Networks to Automatic Labeling of Vehicle Dynamic Response Data for Bridges. Sensors. 2022; 22(9):3486. https://doi.org/10.3390/s22093486

Chicago/Turabian Style

Shin, Ryota, Yukihiko Okada, and Kyosuke Yamamoto. 2022. "Application of C-LSTM Networks to Automatic Labeling of Vehicle Dynamic Response Data for Bridges" Sensors 22, no. 9: 3486. https://doi.org/10.3390/s22093486

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