title: Vehicle Signal Analysis Using Artificial Neural Networks for a Bridge Weigh-in-Motion System.
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.
authors.affiliation: Seoul National University of Technology / Seoul, Korea; E-Mail:  skkim@snut.ac.kr.
authors.forename: Sungkon
authors.initials: S
authors.lastname: Kim
authors.forename: Jungwhee
authors.initials: J
authors.lastname: Lee
authors.forename: Min-Seok
authors.initials: MS
authors.lastname: Park
authors.forename: Byung-Wan
authors.initials: BW
authors.lastname: Jo