Cluster-based Deterioration Prediction of Composite Pavements with Incorporation of Flooding

Natural disasters lead to severe deterioration of valuable highway assets, including pavements that should quickly return to service after extreme events such as flooding. Various prediction models were developed to predict pavement performance for several purposes, including maintenance management, budget allocation, and investment strategy. However, limited studies focused on developing a deterioration model for flood-affected composite pavements. This paper proposes a framework for evaluating and predicting the change in composite pavements' roughness due to the flood probability. To this end, a cluster-based pavement deterioration model was developed and applied to a case study of 102 pavement sections from the LTPP database in the United States' eastern region from 2015 to 2019. Then, we used Markov Chain and Monte Carlo simulation on three generated clusters to predict the flood impact on three groups of pavements with different characteristics. The pivotal role of the proposed framework is predicting IRI values due to varying flooding probabilities in different pavement clusters. The results indicate that the pavement tends to deteriorate faster in the initial post-flood years if subjected to heavy or moderate traffic loading and precipitation conditions. This rate will tend to decrease as the age of the pavement increases. For the sections subjected to low traffic loading and low precipitation, the rate of deterioration for the initial post-flood years is less. Still, it will tend to increase as the age of pavement increases.