A Bayesian Approach towards Modelling the Interrelationships of Pavement Deterioration Factors

In this study, Bayesian Belief Networks (BBN) are proposed to model the relationships between factors contributing to pavement deterioration, where their values are probabilistically estimated based on their interdependencies. Such probabilistic inferences are deemed to provide a reasonable alternative over costly data collection campaigns and assist in road condition diagnoses and assessment efforts in cases where data are only partially available. The BBN models examined in this study are based on a vast database of pavement deterioration factors including road distress data, namely cracking, deflection, the International Roughness Index (IRI) and rutting, from major road sections in the United Arab Emirates (UAE) along with the corresponding traffic and climatic factors. The dataset for the analysis consisted of 3272 road sections, each of 10 m length. The test results showed that the most critical parameter representing the whole process of road deterioration is the IRI with the highest nodal force. Additionally, IRI is strongly correlated with rutting and deflection, with mutual information of 0.147 and 0.143, respectively. Furthermore, a Bayesian network structure with a contingency table fit of over 90% illustrates how the road distress parameters change in the presence of external factors, such as traffic and climatic conditions.

Automated summary

This study proposes the use of Bayesian Belief Networks (BBN) to model the relationships between factors contributing to pavement deterioration, with their values estimated probabilistically based on their interdependencies. The study examines BBN models based on a large database of pavement deterioration factors, including road distress data, traffic, and climatic factors. The results show that the most critical parameter representing the road deterioration process is the International Roughness Index (IRI), which is strongly correlated with rutting and deflection. Furthermore, a Bayesian network structure illustrates how road distress parameters change in the presence of external factors such as traffic and climatic conditions.