Importance of the Tail in Truck Weight Modeling for Bridge Assessment

O’Brien, Eugene J.; Enright, Bernard; Getachew, Abraham

doi:10.1061/(asce)be.1943-5592.0000043

Cited by 55 publications

(34 citation statements)

References 8 publications

(7 reference statements)

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“…As convergence may be slow, Caprani (2005) and OBrien et al (2010) have fitted block maximum LE data with a 'Normal to the power of n', i.e., a Normal distribution raised to some power, n, whose value is found by fitting to the data. This has merit for smaller data samples.…”

Section: Block Maximum -Extreme Value Distributionsmentioning

confidence: 99%

A review of probabilistic methods of assessment of load effects in bridges

O’Brien

Schmidt²,

Hajializadeh

et al. 2015

Structural Safety

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This paper reviews a range of statistical approaches to illustrate the influence of data quality and quantity on the probabilistic modelling of traffic load effects. It also aims to demonstrate the importance of long-run simulations in calculating characteristic traffic load effects. The popular methods of Peaks Over Threshold and Generalized Extreme Value are considered but also other methods including the Box-Cox approach, fitting to a Normal distribution and the Rice formula. For these five methods, curves are fitted to the tails of the daily maximum data.Bayesian Updating and Predictive Likelihood are also assessed, which require the entire data for fittings. The accuracy of each method in calculating 75-year characteristic values and probability of failure, using different quantities of data, is assessed. The nature of the problem is first introduced by a simple numerical example with a known theoretical answer. It is then extended to more realistic problems, where long-run simulations are used to provide benchmark results, against which each method is compared. Increasing the number of data in the sample results in higher accuracy of approximations but it is not able to completely eliminate the uncertainty associated with the extrapolation. Results also show that the accuracy of estimations of characteristic value and probabilities of failure are more a function of data quality than extrapolation technique. This highlights the importance of long-run simulations as a means of reducing the errors associated with the extrapolation process.

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Section: Block Maximum -Extreme Value Distributionsmentioning

confidence: 99%

A review of probabilistic methods of assessment of load effects in bridges

O’Brien

Schmidt²,

Hajializadeh

et al. 2015

Structural Safety

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show abstract

“…For Gross Vehicle Weight (GVW) several models are proposed in the literature (Jacob 1991;Kennedy et al 1992;Cooper 1995;Crespo-Minguillón and Casas 1997;Bailey and Bez 1999;O'Connor and OBrien 2005;OBrien et al 2006). In this simulation, the GVW and number of axles for each truck is simulated using the 'semiparametric' approach proposed by OBrien et al (2010). Below a specified GVW threshold, an empirical (bootstrap) bivariate distribution is used to generate GVW and number of axles.…”

Section: Realistic Traffic Load Modelmentioning

confidence: 99%

Nonlinear analysis of isotropic slab bridges under extreme traffic loading

et al. 2015

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Probabilistic analysis of traffic loading on a bridge traditionally involves an extrapolation from measured or simulated load effects to a characteristic maximum value. In recent years, long run simulations, whereby thousands of years of traffic are simulated, have allowed researchers to gain new insights into the nature of the traffic scenarios that govern at the limit state. For example, mobile cranes and low-loaders, sometimes accompanied by a common articulated truck, have been shown to govern in most cases. In this paper, the extreme loading scenarios identified in the long-run simulation are applied to a non-linear, two-dimensional (2D) plate finite element model. For the first time, the loading scenarios that govern in 2D nonlinear analyses are found and compared to those that govern for 2D linear and one-dimensional (1D) linear and nonlinear analyses. Results show that, for an isotropic slab, the governing loading scenarios are similar to those that govern in simple 1D (beam) models. Furthermore, there are only slight differences in the critical positions of the vehicles. It is also evident that the load effects causing failure in the 2D linear elastic plate models are significantly lower, i.e., 2D linear elastic analysis is more conservative than both 2D nonlinear and 1D linear and nonlinear analyses.

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“…3. While the characteristic maximum 75-year load effect could have been taken directly, accuracy is improved by a best fitting of a Weibull distribution to the tail of the data (OBrien et al 2010). In this case, least squares fitting is used to find the best fit to the top 2√n of the n data points (Castillo 1988).…”

Section: Monte Carlo Simulationmentioning

confidence: 99%