Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion Systemopen access
- Authors
- Kim, Sungkon; Lee, Jungwhee; Park, Min-Seok; Jo, Byung-Wan
- Issue Date
- Oct-2009
- Publisher
- MDPI
- Keywords
- bridge weigh-in-motion (B-WIM); artificial neural network (ANN); cable-stayed bridge; vehicle information
- Citation
- SENSORS, v.9, no.10, pp.7943 - 7956
- Indexed
- SCIE
SCOPUS
- Journal Title
- SENSORS
- Volume
- 9
- Number
- 10
- Start Page
- 7943
- End Page
- 7956
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/176065
- DOI
- 10.3390/s91007943
- ISSN
- 1424-8220
- 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.
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Collections - 서울 공과대학 > 서울 건설환경공학과 > 1. Journal Articles
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