🇦🇪Predicting the “Cracking” Behavior of Road Networks in UAE with Bayesian Belief Networks

Presented at the 9th Annual BayesiaLab Conference on October 12, 2021.

Abstract

Cracking is one of the major factors which leads to the deterioration of road structures. Globally, United Arab Emirates is a country with high-standard road networks. Highway organizations take the initiative to measure the cracking and confirm whether it is within the prescribed limit. However, such monitoring activities are expensive in terms of cost, labor, and machinery, which ultimately leads to failure in the timely repair and maintenance activities of the roads. This results in a reduction of the service life of the pavements. This study aims to develop a solution for this problem by studying the historical data of factors that influence cracking in roads. To perform this, data related to major road networks in the country are collected from the highway agency. The data include environmental factors, traffic intensity, and factors like road type and age of the road. A Supervised Learning algorithm will determine the role of each factor that contributes to cracking. Once the significance of each factor is analyzed, further analysis based on a dynamic Bayesian network will aid in estimating the future values of cracking on the roads without measuring it. This study thus can be a major contribution in the transportation field to improve the quality of road networks.

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About the Presenter

Ms. Babitha did her B.Tech in Civil Engineering and M.Tech in Construction Engineering and Management from India. She has worked as a Research Assistant at UAE University in the Civil and Environmental Engineering Department, and her work was mainly focused on applying Artificial Intelligence techniques in the Civil Engineering field. Currently, she is doing a Ph.D. at UAE University on a topic involving the application of Bayesian networks.

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