Simplified probabilistic model for maximum traffic load from weigh-in-motion data

Chacón, Miriam Soriano; Rius, Joan Ramón Casas; Ghosn, Michel

doi:10.1201/b17063-41

Cited by 3 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While the effects of individual trucks or a combination of trucks may be represented by histograms such as those in Figures 2, 3 and 5 or presented in mathematical expressions (such as Equation 1, bridge engineers are interested in ensuring the safety of bridges when they are exposed to the maximum possible loads that may take place within the their service lives. The projection of the load effect to find the maximum load in a specific time interval requires the application of extreme value theory which can be greatly simplified if the upper 5% of the load effect histograms can be represented by a normal distribution (Ghosn et al, 2011;Sivakumar, et al, 2011;Soriano et al, 2014Soriano et al, ,2016. In fact, the maximum load effect of data that fit a normal distribution is known to follow an Extreme Value Type I (Gumbel) distribution the statistical parameters of which are obtained by means of the following equations (Ang & Tang, 2007):…”

Section: Projection Of Maximum Load Effect For Different Service Livesmentioning

confidence: 99%

Methodology for development of live load models for refined analysis of short and medium-span highway bridges

Anitori

Casas

Ghosn

2017

Structure and Infrastructure Engineering

View full text Add to dashboard Cite

The accuracy of bridge system safety evaluations and reliability assessments obtained through refined structural and Finite Element (FE) analyses depends not only on the accuracy of the structural model itself but also on the proper modeling of the maximum traffic loads. While current code-specified live load models were calibrated to properly reflect the safety levels of bridge structures analyzed using the simplified methods adopted in bridge design and evaluation manuals, these load models may not lead to accurate results when implemented during refined structural analysis procedures. Accurate live load models need to be defined based on a statistical analysis of real traffic data. This paper describes a method to calibrate appropriate live load models that can be used for advanced analyses of highway bridges. The calibration procedure is demonstrated using actual truck data collected at a representative weigh-in-motion (WIM) station in New York State. Extreme value theory is used to project traffic load effects and calculate the maximum effect expected to take place over different bridge service periods. The proposed calibration methodology is applicable for developing live load models for different bridge types and different design/assessment codes or standards. Live load models obtained using the proposed calibration procedure are readily implementable for deterministic refined analyses of highway bridges to produce similar results to those of complex traffic load simulations. Examples are presented that describe how results of such calibrated live load models would be used in engineering practice.

show abstract

Section: Projection Of Maximum Load Effect For Different Service Livesmentioning

confidence: 99%

Methodology for development of live load models for refined analysis of short and medium-span highway bridges

Anitori

Casas

Ghosn

2017

Structure and Infrastructure Engineering

View full text Add to dashboard Cite

show abstract

“…Vehicle load is one of the most significant factors for bridge design, safety assessment, and fatigue analysis [1][2][3][4][5]. Overloaded heavy vehicles is the primary reason for the deterioration of structural components and the degradation of the bridge's overall state [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Threshold Selection for POT Framework in the Extreme Vehicle Loads Analysis Based on Multiple Criteria

Qiu

2018

Shock and Vibration

View full text Add to dashboard Cite

Extreme value of vehicle load plays an important role in bridge design and risk assessment. Peaks over threshold (POT) is a method commonly used in extreme load estimation. The selection of thresholds for the POT method is extremely crucial, but the selected optimal threshold varies under different test criteria. Therefore, a method to select the suitable threshold is developed based on multiple criteria decision analysis (MCDA) in the paper. In MCDA, Chi-Square (χ2) test, Kolmogorov–Smirnov (K–S) test, and Root Mean Square Error (RMSE) in probability distribution functions (PDF) are employed as the test criteria and the weight of these criteria is calculated using the entropy method. Finally, vehicle loads obtained from simulation and field measurement are adopted to validate the effectiveness and feasibility of the proposed method. The results indicate that the proposed MCDA is a useful and complementary tool for threshold selection in the extreme value analysis.

show abstract

“…However, application of the SHM system is limited by its expensive cost and limited specified objectives. With the development of sensor technologies, the site-specific weigh-in-motion (WIM) system, which is initially developed for traffic management, has been widely used for statistical analysis of traffic loads [10]. Therefore, integration of site-specific WIM measurements and the finite element (FE) method becomes a practical approach for fatigue reliability assessment of in-service bridges.…”

Section: Introductionmentioning

confidence: 99%

Fatigue Reliability Assessment of Orthotropic Bridge Decks under Stochastic Truck Loading

Liu

Xiao

et al. 2016

Shock and Vibration

View full text Add to dashboard Cite

A steady traffic growth has posed a threat to the fatigue safety of existing bridges. Uncertainties in traffic flows add to the challenge of an accurate fatigue safety assessment. This article utilizes a stochastic traffic load model to evaluate the fatigue reliability of orthotropic steel bridge decks. The traffic load model is simulated by site-specific weigh-in-motion measurements. A response surface method is presented to solve the time-consuming problem caused by hotspot stress simulations in the finite element model. Applications of the stochastic traffic load model for probabilistic modeling and fatigue reliability assessment are demonstrated in the case study of a steel box-girder bridge. Numerical results indicate that the growth rate of the gross vehicle weight leads to a rapid decrease of the fatigue reliability in comparison to the traffic volume growth. Even though the traffic volume growth is rapid, the control of overloaded trucks in comparison to the traffic volume is an effective way to ensure the fatigue safety of the steel bridges.

show abstract

Simplified probabilistic model for maximum traffic load from weigh-in-motion data

Cited by 3 publications

References 10 publications

Methodology for development of live load models for refined analysis of short and medium-span highway bridges

Methodology for development of live load models for refined analysis of short and medium-span highway bridges

Threshold Selection for POT Framework in the Extreme Vehicle Loads Analysis Based on Multiple Criteria

Fatigue Reliability Assessment of Orthotropic Bridge Decks under Stochastic Truck Loading

Contact Info

Product

Resources

About