الفهرس 
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Abstract
At the present time, all infrastructure assets require management systems to monitor their performance over time and keep them in a good functional and serviceable condition. These systems act as tools for planning, applying, and revising maintenance and rehabilitation (M&R) programs. One of the most valuable strategic assets in any country is its road network. Recently, highway agencies have become in need to enhance their Pavement Management Systems (PMSs) using sound engineering and economic principles that find more prudent solutions to support wise investment choices and preserve the value of infrastructure assets. Pavement infrastructure deterioration is a dynamic, complicated, and stochastic process since it is a result of the combined impact of various factors such as traffic loading, environmental condition, structural capacities, and some unobserved factors. Reliable and accurate predictions of pavement infrastructure performance boosts saving significant amounts of money for pavement infrastructure management agencies by adopting better planning, maintenance, and rehabilitation activities. International Roughness Index (IRI) is a pavement performance indicator which reflects pavement condition and comfort level of road users. As pavement performance prediction models are one of the most important and fundamental components in any PMS, this study focuses on comparing the master sigmoidal curve-based model as a deterministic technique with the Markov chain-based model as a probabilistic technique for the prediction of the International Roughness Index (IRI) as a pavement performance indicator. The IRI data obtained from the Long-Term Pavement Performance (LTPP) Program, is used to evaluate and compare both methods. In this research, 44 flexible pavement sections from the GPS-1 experiment incorporating 432 IRI measurements ranging from 0.575 to 4.715 m/Km are selected. Another dataset of SPS experiment is chosen. This second dataset contains SPS-1 sections remaining in the LTPP study, which consists of 55 sections incorporating 766 IRI records ranging from 0.474 to 2.856 m/km. Results show that the predictive ability of the investigated models for the selected LTPP data was good with a reasonable coincidence ratio between the predicted results of the two modeling techniques of about 65 percent for the GPS-1 and about 50 percent for the SPS-1 sections. Moreover, Markov-chain provides a prediction tool / method that is not mathematically complicated relative to other deterministic techniques and does not matter how much data is available. In contrast it is simpler than other deterministic techniques and can be applied by several methods using different sizes of historical data either small or large in size data. Thus, it can be considered optimal, faster and practical method from the engineer’s perspective.