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Analysis of axle and vehicle load properties through Bayesian Networks based on Weigh-in-Motion data

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  • Morales-Nápoles, Oswaldo
  • Steenbergen, Raphaël D.J.M.

Abstract

Weigh-in-Motion (WIM) systems are used, among other applications, in pavement and bridge reliability. The system measures quantities such as individual axle load, vehicular loads, vehicle speed, vehicle length and number of axles. Because of the nature of traffic configuration, the quantities measured are evidently regarded as random variables. The dependence structure of the data of such complex systems as the traffic systems is also very complex. It is desirable to be able to represent the complex multidimensional-distribution with models where the dependence may be explained in a clear way and different locations where the system operates may be treated simultaneously.

Suggested Citation

  • Morales-Nápoles, Oswaldo & Steenbergen, Raphaël D.J.M., 2014. "Analysis of axle and vehicle load properties through Bayesian Networks based on Weigh-in-Motion data," Reliability Engineering and System Safety, Elsevier, vol. 125(C), pages 153-164.
    • Handle: RePEc:eee:reensy:v:125:y:2014:i:c:p:153-164
    DOI: 10.1016/j.ress.2014.01.018
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    Cited by:

    1. Hanea, Anca & Morales Napoles, Oswaldo & Ababei, Dan, 2015. "Non-parametric Bayesian networks: Improving theory and reviewing applications," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 265-284.

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