Developing Pavement Distress Deterioration Models for Pavement Management System Using Markovian Probabilistic Process

Abstract

In the state of Colorado, the Colorado Department of Transportation (CDOT) utilizes their pavement management system (PMS) to manage approximately 9,100 miles of interstate, highways, and low-volume roads. ree types of deterioration models are currently being used in the existing PMS: site-speci c, family, and expert opinion curves. ese curves are developed using deterministic techniques. In the deterministic technique, the uncertainties of pavement deterioration related to tra c and weather are not considered. Probabilistic models that take into account the uncertainties result in more accurate curves. In this study, probabilistic models using the discrete-time Markov process were developed for ve distress indices: transverse, longitudinal, fatigue, rut, and ride indices, as a case study on low-volume roads. Regression techniques were used to develop the deterioration paths using the predicted distribution of indices estimated from the Markov process. Results indicated that longitudinal, fatigue, and rut indices had very slow deterioration over time, whereas transverse and ride indices showed faster deterioration. e developed deterioration models had the coe cient of determination (R 2 ) above 0.84. As probabilistic models provide more accurate results, it is recommended that these models be used as the family curves in the CDOT PMS for low-volume roads.

A Two‐Stage Stochastic Model for Maintenance and Rehabilitation Planning of Pavements

“…Therefore, in order to make the comparison between the deterministic and stochastic models possible, the results of the first stage decision variable of the two-stage stochastic model were compared to the decision variable of deterministic model. Moreover, in order to assess the impact of technical constraints on the results, the deterministic model was also solved without any technical constraints addressed by (31) to (44). As such, adding technical constraints to the deterministic model resulted in a 91% decrease in runtime for solving the problem.…”

“…Durango-Cohen took pavement deterioration models as nondeterministic models and evaluated the M&R policies efficiently when imprecise pavement deterioration models are available [27]. Some researchers found the uncertainty in pavement deterioration models as function of uncertainty in pavement structural design, traffic and climatic conditions, and pavement age [28][29][30][31][32]. However, it is important not to restrict the uncertainty to the pavement deterioration [23].…”

Soft Computing Models to Predict Pavement Roughness: A Comparative Study

“…Prediction models are indispensable components of a PMS, and their prediction accuracy is vital for the efficient management of the road infrastructure [10]. For instance, by minimizing the prediction error of pavement roughness, agencies can achieve significant budget savings through timely intervention and accurate planning.…”

An Integrated Multi-Objectives Optimization Approach on Modelling Pavement Maintenance Strategies for Pavement Sustainability

“…A popular and widely applied approach, the Markov Chain (MC), has been developed to address the uncertainty issue for predicting future pavement conditions. Widely existing methods applied the MC to predict future pavement conditions by the state transition probability matrix (TPM) (Pérez-Acebo et al, 2018;Saha et al, 2017;Tabatabaee & Ziyadi, 2013). Those include the research conducted by Mandiartha et al (2017), which applied the MC to model the road pavement deterioration process.…”