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Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System
Seoul National University of Technology / Seoul, Korea
Dankook University / Yongin-si, Gyeonggi-do, Korea
Korea Expressway Corporation / Sungnam-si, Gyeonggi-do, Korea
Hanyang University / Seoul, Korea
* Author to whom correspondence should be addressed.
Received: 31 July 2009; in revised form: 22 September 2009 / Accepted: 24 September 2009 / Published: 12 October 2009
Abstract: This paper describes the procedures for development of signal analysis algorithms using artificial neural networks for Bridge Weigh-in-Motion (B-WIM) systems. Through the analysis procedure, the extraction of information concerning heavy traffic vehicles such as weight, speed, and number of axles from the time domain strain data of the B-WIM system was attempted. As one of the several possible pattern recognition techniques, an Artificial Neural Network (ANN) was employed since it could effectively include dynamic effects and bridge-vehicle interactions. A number of vehicle traveling experiments with sufficient load cases were executed on two different types of bridges, a simply supported pre-stressed concrete girder bridge and a cable-stayed bridge. Different types of WIM systems such as high-speed WIM or low-speed WIM were also utilized during the experiments for cross-checking and to validate the performance of the developed algorithms.
Keywords: bridge weigh-in-motion (B-WIM); artificial neural network (ANN); cable-stayed bridge; vehicle information
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MDPI and ACS Style
Kim, S.; Lee, J.; Park, M.-S.; Jo, B.-W. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System. Sensors 2009, 9, 7943-7956.
Kim S, Lee J, Park M-S, Jo B-W. Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System. Sensors. 2009; 9(10):7943-7956.
Kim, Sungkon; Lee, Jungwhee; Park, Min-Seok; Jo, Byung-Wan. 2009. "Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System." Sensors 9, no. 10: 7943-7956.