Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System
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 fr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2009-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/9/10/7943/ |
| _version_ | 1798037711776382976 |
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| author | Min-Seok Park Byung-Wan Jo Sungkon Kim Jungwhee Lee |
| author_facet | Min-Seok Park Byung-Wan Jo Sungkon Kim Jungwhee Lee |
| author_sort | Min-Seok Park |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T21:30:17Z |
| format | Article |
| id | doaj.art-5d790566d8a34b2fa939b473310398b2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:30:17Z |
| publishDate | 2009-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5d790566d8a34b2fa939b473310398b22022-12-22T04:02:02ZengMDPI AGSensors1424-82202009-10-019107943795610.3390/s91007943Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion SystemMin-Seok ParkByung-Wan JoSungkon KimJungwhee LeeThis 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.http://www.mdpi.com/1424-8220/9/10/7943/bridge weigh-in-motion (B-WIM)artificial neural network (ANN)cable-stayed bridgevehicle information |
| spellingShingle | Min-Seok Park Byung-Wan Jo Sungkon Kim Jungwhee Lee Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System Sensors bridge weigh-in-motion (B-WIM) artificial neural network (ANN) cable-stayed bridge vehicle information |
| title | Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System |
| title_full | Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System |
| title_fullStr | Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System |
| title_full_unstemmed | Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System |
| title_short | Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System |
| title_sort | vehicle signal analysis using artificial neural networks for a bridge weigh in motion system |
| topic | bridge weigh-in-motion (B-WIM) artificial neural network (ANN) cable-stayed bridge vehicle information |
| url | http://www.mdpi.com/1424-8220/9/10/7943/ |
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