Title: A parametric study of bridge load effect under stochastic vehicular load

Authors: He-Qing Mu; Hou-Zuo Guo; Tian-Yu Zhang; Cheng Su

Addresses: School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou 510640, China ' School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China ' School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China ' School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou 510640, China

Abstract: Bridge vehicular load effect inference is one of the key factors in bridge reliability and life-cycle assessment. The stochastic vehicular load can be decomposed into two parts: 1) the random vehicular inflow, describing the information about the properties of the inflow vehicles entering the bridge; 2) the stochastic vehicular flow, describing the information about the vehicle-following pattern of vehicles on the bridge. In order to investigate how the bridge load effect is affected by the stochastic vehicular load, two key parameters of the stochastic vehicular load are selected: 1) the probability of the existence of vehicle (PoV), controlling the traffic volume of the vehicular inflow entering the bridge; 2) the probability of random slowing down (PoSD), controlling the vehicle-following pattern of the stochastic vehicular flow. With different values of the PoV and the PoSD, the samples of the vehicular load effects are achieved by embedding the samples of the stochastic vehicular load and the influence lines of the load effects. The Gaussian process regression (GPR) is utilised to obtain the relations between two parameters of the stochastic vehicular load (the PoV and the PoSD) and the statistical moments [the mean and the standard deviation (STD)] of the simulated load effect samples. It turns out that the load effect is interactively influenced by the PoV and the PoSD.

Keywords: bridge vehicular load effect; Gaussian process regression; GPR; Nagel-Schreckenberg model; uncertainty.

DOI: 10.1504/IJLCPE.2019.099888

International Journal of Lifecycle Performance Engineering, 2019 Vol.3 No.1, pp.77 - 90

Received: 23 May 2018
Accepted: 17 Oct 2018
Published online: 26 May 2019 *