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Structural Engineering and Mechanics
  Volume 20, Number 1, May10 2005 , pages 85-110
DOI: https://doi.org/10.12989/sem.2005.20.1.085
 


Statistical models from weigh-in-motion data
Tommy H.T. Chan, T. J. Miao and Demeke B. Ashebo

 
Abstract
    This paper aims at formulating various statistical models for the study of a ten year Weigh-in-Motion (WIM) data collected from various WIM stations in Hong Kong. In order to study the bridge live load model it is important to determine the mathematical distributions of different load affecting parameters such as gross vehicle weights, axle weights, axle spacings, average daily number of trucks etc. Each of the above parameters is analyzed by various stochastic processes in order to obtain the mathematical distributions and the Maximum Likelihood Estimation (MLE) method is adopted to calculate the statistical parameters, expected values and standard deviations from the given samples of data. The Kolmogorov-Smirnov (K-S) method of approach is used to check the suitability of the statistical model selected for the particular parameter and the Monte Carlo method is used to simulate the distributions of maximum value stochastic processes of a series of given stochastic processes. Using the statistical analysis approach the maximum value of gross vehicle weight and axle weight in bridge design life has been determined and the distribution functions of these parameters are obtained under both free-flowing traffic and dense traffic status. The maximum value of bending moments and shears for wide range of simple spans are obtained by extrapolation. It has been observed that the obtained maximum values of the gross vehicle weight and axle weight from this study are very close to their legal limitations of Hong Kong which are 42 tonnes for gross weight and 10 tonnes for axle weight.
 
Key Words
    weigh in motion; stochastic process; Monte Carlo method; gross vehicle weight; axle weight; inattentive traffic status; dense traffic status.
 
Address
Department of Civil and Structural Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
 

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