Statistical analysis and probabilistic modeling of WIM monitoring data of an instrumented arch bridge

- Journal title : Smart Structures and Systems
- Volume 17, Issue 6, 2016, pp.1087-1105
- Publisher : Techno-Press
- DOI : 10.12989/sss.2016.17.6.1087

Title & Authors

Statistical analysis and probabilistic modeling of WIM monitoring data of an instrumented arch bridge

Ye, X.W.; Su, Y.H.; Xi, P.S.; Chen, B.; Han, J.P.;

Ye, X.W.; Su, Y.H.; Xi, P.S.; Chen, B.; Han, J.P.;

Abstract

Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike`s information criterion (AIC) values.

Keywords

structural health monitoring;weigh-in-motion;gross vehicle weight;finite mixture distributions;mixture parameter estimation;genetic algorithm;

Language

English

Cited by

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