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Methodology for Pavement Design Reliability and Back Analysis Using Markov Chain Monte Carlo Simulation

Dilip, Deepthi Mary and Babu, Sivakumar GL (2013) Methodology for Pavement Design Reliability and Back Analysis Using Markov Chain Monte Carlo Simulation. In: JOURNAL OF TRANSPORTATION ENGINEERING-ASCE, 139 (1). pp. 65-74.

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Official URL: http://dx.doi.org/10.1061/(ASCE)TE.1943-5436.00004...


Given the increasing cost of designing and building new highway pavements, reliability analysis has become vital to ensure that a given pavement performs as expected in the field. Recognizing the importance of failure analysis to safety, reliability, performance, and economy, back analysis has been employed in various engineering applications to evaluate the inherent uncertainties of the design and analysis. The probabilistic back analysis method formulated on Bayes' theorem and solved using the Markov chain Monte Carlo simulation method with a Metropolis-Hastings algorithm has proved to be highly efficient to address this issue. It is also quite flexible and is applicable to any type of prior information. In this paper, this method has been used to back-analyze the parameters that influence the pavement life and to consider the uncertainty of the mechanistic-empirical pavement design model. The load-induced pavement structural responses (e.g., stresses, strains, and deflections) used to predict the pavement life are estimated using the response surface methodology model developed based on the results of linear elastic analysis. The failure criteria adopted for the analysis were based on the factor of safety (FOS), and the study was carried out for different sample sizes and jumping distributions to estimate the most robust posterior statistics. From the posterior statistics of the case considered, it was observed that after approximately 150 million standard axle load repetitions, the mean values of the pavement properties decrease as expected, with a significant decrease in the values of the elastic moduli of the expected layers. An analysis of the posterior statistics indicated that the parameters that contribute significantly to the pavement failure were the moduli of the base and surface layer, which is consistent with the findings from other studies. After the back analysis, the base modulus parameters show a significant decrease of 15.8% and the surface layer modulus a decrease of 3.12% in the mean value. The usefulness of the back analysis methodology is further highlighted by estimating the design parameters for specified values of the factor of safety. The analysis revealed that for the pavement section considered, a reliability of 89% and 94% can be achieved by adopting FOS values of 1.5 and 2, respectively. The methodology proposed can therefore be effectively used to identify the parameters that are critical to pavement failure in the design of pavements for specified levels of reliability. DOI: 10.1061/(ASCE)TE.1943-5436.0000455. (C) 2013 American Society of Civil Engineers.

Item Type: Journal Article
Additional Information: Copyright for this article belongs to ASCE-AMER SOC CIVIL ENGINEERS, USA
Keywords: Mechanistic-empirical;Back analysis;Markov chain Monte Carlo;Reliability;RSM;Bayesian analysis
Department/Centre: Division of Mechanical Sciences > Civil Engineering
Depositing User: Francis Jayakanth
Date Deposited: 18 Feb 2013 08:57
Last Modified: 18 Feb 2013 08:57
URI: http://eprints.iisc.ac.in/id/eprint/45777

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