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Estimating the frequency of traffic overloading on road bridges

Journal of Traffic and Transportation Engineering(English Edition) 2024年第4期11卷 776-796页

作者： Roberto Ventura Benedetto Barabino giulio maternini Department of Civil EnvironmentalArchitectural Engineering and MathematicsUniversity of BresciaBrescia 25123Italy

Load limits,which appear to be routinely exceeded by trucks,occasionally result in road bridge ***,predicting failures is crucial for safeguarding road *** studies have largely focused on forecasting bridge failure event probability using the reliability analysis method,whilst occasionally accounting for vehicular overloading *** recently,a study has investigated design traffic overloading event frequency using generalised linear regression models(GLRMs),including a power component and negative binomial regressions(NBRs).However,as far as the authors know,artificial neural network models(ANNMs)have never been applied to this *** paper is an attempt to fill in these *** a frequencybased metric of traffic overloading was adopted as a driver of failure ***,two alternative‘frequency'models were specified,calibrated,and *** former was based on a GLRM,the latter on ***,these models were compared using regression plots(RPs),measures of errors(Mo Es)and the ratio between the number of observed vs predicted design load overcoming events to evaluate their *** models analysed more than 2 million weigh-in-motion(WIM)data records from a pilot station on a bridge on a heavily used ring road in Brescia(Italy).Results showed that ANNMs outperformed *** have a higher correlation coefficient(between predicted and target frequencies),lower Mo Es,and a closer-to-unity ratio(between predicted and target frequencies).These findings may increase prediction accuracy of design traffic overloading events and give road authorities more effective traffic management to protect bridges from load hazards.

关键词： Road bridges Traffic load hazard Econometry Machine learning Weigh-in-motion Big-data analysis

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