Sensors 2009, 9(10), 7943-7956; https://doi.org/10.3390/s91007943
Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System
1
Seoul National University of Technology / Seoul, Korea
2
Dankook University / Yongin-si, Gyeonggi-do, Korea
3
Korea Expressway Corporation / Sungnam-si, Gyeonggi-do, Korea
4
Hanyang University / Seoul, Korea
*
Author to whom correspondence should be addressed.
Received: 31 July 2009 / Revised: 22 September 2009 / Accepted: 24 September 2009 / Published: 12 October 2009
(This article belongs to the Special Issue Neural Networks and Sensors)
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. View Full-TextKeywords:
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.