Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System

Sensors

Sungkon Kim¹  Jungwhee Lee³  Min-Seok Park² 

[1] Seoul National University of Technology / Seoul, Korea; E-Mail:;Korea Expressway Corporation / Sungnam-si, Gyeonggi-do, Korea; E-Mail:;Dankook University / Yongin-si, Gyeonggi-do, Korea

关键词: bridge weigh-in-motion (B-WIM);    artificial neural network (ANN);    cable-stayed bridge;    vehicle information;   

DOI  :  10.3390/s91007943

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.