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Article

The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods

1
National Center for Research on Earthquake Engineering, Taipei 106, Taiwan
2
Hydrotech Research Institute, National Taiwan University, Taipei 106, Taiwan
3
Department of Civil Engineering, National Taiwan University, Taipei 106, Taiwan
4
National Center for High-Performance Computing, Hsinchu 300, Taiwan
5
Department of Civil Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Antonella D’Alessandro and Yoji Okabe
Sensors 2021, 21(14), 4942; https://doi.org/10.3390/s21144942
Received: 14 May 2021 / Revised: 16 July 2021 / Accepted: 17 July 2021 / Published: 20 July 2021
(This article belongs to the Special Issue Smart Materials for Structural Health Monitoring and Damage Detection)
Scour around bridge piers remains the leading cause of bridge failure induced in flood. Floods and torrential rains erode riverbeds and damage cross-river structures, causing bridge collapse and a severe threat to property and life. Reductions in bridge-safety capacity need to be monitored during flood periods to protect the traveling public. In the present study, a scour monitoring system designed with vibration-based arrayed sensors consisting of a combination of Internet of Things (IoT) and artificial intelligence (AI) is developed and implemented to obtain real-time scour depth measurements. These vibration-based micro-electro-mechanical systems (MEMS) sensors are packaged in a waterproof stainless steel ball within a rebar cage to resist a harsh environment in floods. The floodwater-level changes around the bridge pier are performed using real-time CCTV images by the Mask R-CNN deep learning model. The scour-depth evolution is simulated using the hydrodynamic model with the selected local scour formulas and the sediment transport equation. The laboratory and field measurement results demonstrated the success of the early warning system for monitoring the real-time bridge scour-depth evolution. View Full-Text
Keywords: bridge failure; scour monitoring; flood; MEMS; deep learning bridge failure; scour monitoring; flood; MEMS; deep learning
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MDPI and ACS Style

Lin, Y.-B.; Lee, F.-Z.; Chang, K.-C.; Lai, J.-S.; Lo, S.-W.; Wu, J.-H.; Lin, T.-K. The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods. Sensors 2021, 21, 4942. https://doi.org/10.3390/s21144942

AMA Style

Lin Y-B, Lee F-Z, Chang K-C, Lai J-S, Lo S-W, Wu J-H, Lin T-K. The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods. Sensors. 2021; 21(14):4942. https://doi.org/10.3390/s21144942

Chicago/Turabian Style

Lin, Yung-Bin, Fong-Zuo Lee, Kuo-Chun Chang, Jihn-Sung Lai, Shi-Wei Lo, Jyh-Horng Wu, and Tzu-Kang Lin. 2021. "The Artificial Intelligence of Things Sensing System of Real-Time Bridge Scour Monitoring for Early Warning during Floods" Sensors 21, no. 14: 4942. https://doi.org/10.3390/s21144942

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