Application of Improved Crack Prediction Methodology in Florida's Highway Network

Abstract

With the growing need to maintain roadway systems despite increasing competition for resources while ensuring safety and comfort for travelers, sound network-level decision making becomes more vital than ever. A stochastic process known as the Markov chain has been used extensively to capture the uncertainty associated with pavement performance over time and to support this critical decision-making process. By application of the Markov chain, this paper investigates the crack histories of flexible pavements to gain insight into the impacts of two primary factors that contribute to the rapid deterioration of surface cracks in flexible pavements: excessive traffic loading and delayed maintenance and rehabilitation. The empirical results of the investigation, obtained by using the data from the Florida Department of Transportation's pavement condition survey database, are presented. The results show that the impacts of the two factors mentioned above are statistically different from one another in terms of the rate of deterioration of Florida's pavements because of cracks. These findings will assist highway authorities in making more timely and efficient network-level decisions.