Vehicle signal analysis using artificial neural networks for a Bridge Weigh-in-Motion system

Sungkon Kim, Jungwhee Lee, Min Seok Park, Byung Wan Jo

Research output: Contribution to journalArticlepeer-review

49 Scopus citations

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.

Original languageEnglish
Pages (from-to)7943-7956
Number of pages14
JournalSensors
Volume9
Issue number10
DOIs
StatePublished - Oct 2009

Keywords

  • Artificial neural network (ANN)
  • Bridge weigh-in-motion (B-WIM)
  • Cable-stayed bridge
  • Vehicle information

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