Abstract
As the distribution function of traffic load effect on bridge structures has always been unknown or very complicated, a probability model of extreme traffic load effect during service periods has not yet been perfectly predicted by the traditional extreme value theory. Here, we focus on this problem and introduce a novel method based on the bridge structural health monitoring data. The method was based on the fact that the tails of the probability distribution governed the behavior of extreme values. The generalized Pareto distribution was applied to model the tail distribution of traffic load effect using the peak-over-threshold method, while the filtered Poisson process was used to model the traffic load effect stochastic process. The parameters of the extreme value distribution of traffic load effect during a service period could be determined by theoretical derivation if the parameters of tail distribution were estimated. Moreover, Bayes’ theorem was applied to update the distribution model to reduce the statistical uncertainty. Finally, the rationality of the proposed method was applied to analyze the monitoring data of concrete-filled steel tube arch bridge suspenders. The results proved that the approach was convenient and found that the extreme value distribution type III might be more suitable as the traffic load effect probability model.
Highlights
Bridge safety has become a public concern after several collapses in recent years [1,2].Nowadays, increased axle load and growth of traffic density are the main causes of bridge accidents worldwide [3,4,5,6]
An updated Bayesian approach for predicting the extreme value distribution of traffic load effect using monitoring data is proposed based on the extreme value theory (EVT) for stochastic processes
The following conclusions can be made: (I) The generalized Pareto distribution (GPD) is suitable for modeling the tail distribution of traffic load effect data
Summary
Bridge safety has become a public concern after several collapses in recent years [1,2].Nowadays, increased axle load and growth of traffic density are the main causes of bridge accidents worldwide [3,4,5,6]. There is an increasing demand for a systematic and efficient safety assessment of bridges to prevent possible disasters [8,9,10,11]. Among all the load effects that must be determined for a bridge assessment, the most variable effect is that induced by traffic load [12]. The accuracy of bridge safety assessments greatly depends on the accuracy of the traffic load effect probability model which describes the uncertainty of the traffic load effect [13,14]. An accurate prediction method for an extreme traffic load effect model is desired, especially for evaluating the safety of existing bridge structures [12,15]
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