A competing Markov model for cracking prediction on civil structures

Cracks on the surface of civil structures (e.g. pavement sections, concrete structures) progress in several formations and under different deterioration mechanisms. In monitoring practice, it is often that cracking type with its worst damage level is selected as a representative condition state, while other cracking types and their damage levels are neglected in records, remaining as hidden information. Therefore, the practice in monitoring has a potential to conceal with a bias selection process, which possibly result in not optimal intervention strategies. In overcoming these problems, our paper presents a non-homogeneous Markov hazard model, with competing hazard rates. Cracking condition states are classified in three types (longitudinal crack, horizontal crack, and alligator crack), with three respective damage levels. The dynamic selection of cracking condition states are undergone a competing process of cracking types and damage levels. We apply a numerical solution using Bayesian estimation and Markov Chain Monte Carlo method to solve the problem of high-order integration of complete likelihood function. An empirical study on a data-set of Japanese pavement system is presented to demonstrate the applicability and contribution of the model.     

Evaluation of simulation uncertainty in accident reconstruction via combining Response Surface Methodology and Monte Carlo Method

This paper focuses on the uncertainty of simulation results in accident reconstruction. Since the Monte Carlo Method (MCM) requires a large number of simulation runs, in order to reduce the simulation time of MCM in evaluating the uncertainty of simulation results, a new method named “Response Surface-Monte Carlo Method (RS-MCM)” was proposed. Firstly, Response Surface Methodology (RSM) was used to obtain an approximate model of the true accident simulation model, and then the uncertainty of simulation results was evaluated by combining this approximate model and MCM. The steps of RS-MCM include the generation of sample sets, the determination of response surface model and the statistical analysis of simulation results. The distribution of reconstruction results was obtained using RS-MCM, which can provide more comprehensive information in traffic accident survey, such as the probability of a simulation result at any given confidence interval falling within an arbitrary interval and so on. Finally, three cases have been employed to evaluate the effectiveness of the proposed RS-MCM.     

Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service

The primary objective of this study was to evaluate the risks of crashes associated with the freeway traffic flow operating at various levels of service (LOS) and to identify crash-prone traffic conditions for each LOS. The results showed that the traffic flow operating at LOS E had the highest crash potential, followed by LOS F and D. The traffic flow operating at LOS B and A had the lowest crash potential. For LOS A and B, the vehicle platoon and abrupt change in vehicle speeds were major contributing factors to crash occurrences. For LOS C, crash risks were correlated with lane-change maneuvers, speed variation, and small headways in traffic. For LOS D, crash risks increased with an increase in the temporal change in traffic flow variables and the frequency of lane-change maneuvers. For LOS E, crash risks were mainly affected by high traffic volumes and oscillating traffic conditions. For LOS F, crash risks increased with an increase in the standard deviation of flow rate and the frequency of lane-change maneuvers. The findings suggested that the mechanism of crashes were quite different across various LOS. A Bayesian random-parameters logistic regression model was developed to identify crash-prone traffic conditions for various LOS. The proposed model significantly improved the prediction performance as compared to the conventional logistic regression model.