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Analysis of axle and vehicle load properties through Bayesian networks based on weigh-in-motion data

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Author: Morales Napoles, O. · Steenbergen, R.D.J.M.
Type:article
Date:2014
Source:Reliability Engineering and System Safety, 125, 153-164
Identifier: 489328
Keywords: Architecture · Bayesian Networks · Bridge reliability · Design loads · Weigh-in-Motion · Axles · Bayesian networks · Vehicles · Bayesian Networks (bns) · Bridge reliability · Design load · Expert judgment technique · Hybrid Bayesian networks · Maximum bending moments · Weigh-in-motion datum · Weigh-in-motion systems · Weigh-in-motion (WIM) · Buildings and Infrastructure · Built Environment · Building Engineering & Civil Engineering · SR - Structural Reliability · TS - Technical Sciences

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 ofaxles. Because ofthe nature ofúamc 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. Bayesian Networks (BNs) are models that comply with the characteristics listed above. In this paper we discuss BN models and results concerning their ability to adequately represent the data. The paper places attention on the construction and use of the models. We discuss applications of the proposed BNs in reliability analysis. In part¡cular we show how the proposed BNs may be used for computing design values for individual axles, vehicle weight and maximum bending moments of bridges in certain time intervals. These estimates have been used to advise authorities with respect to bridge reliability. Directions as to how the model may be extended to include locations where the WIM system does not operate are given whenever possible, These ideas benefit from structured expert judgment techniques previously used to quantify Hybrid Bayesian Networks (HBNS) with success.