Predictive approach for post-covid 19 evolution study of the pavement surface deterioration based on visual inspection results

Abstract

This study proposes a statistical approach to examine pavement surface deterioration tendencies resulting from the COVID-19 pandemic. The road inspection results, the historical road databases, and the condition analysis make pavement management a major challenge for managers in Morocco as well as around the world. The decrease in traffic, the maintenance stoppages, the difficulties in obtaining field information, all these imperative ingredients are a consequence of the covid-19 pandemic. In this respect, historical analysis, remote monitoring and damage prediction have become increasingly important. In collaboration with the Moroccan National Center for Road Research, this study examines the impact of pandemic-induced lockup on the variation of three important pathologies: pullouts, cracks and potholes, based on the results of a visual inspection and the results of deflection and evenness performed in 2020 on a 50 km long section connecting Meknes and Khemisset cities. First, the reduction of data based on deterioration represented in four levels (A, B, C and D), second, the comparison of the different pathologies before and after the pandemic, the impact of the pandemic on the pavement quality, and finally, a prediction of the progression of the pathology using the linear regression method. This study will help decision makers to take into account pandemics and health failures in their pavement management approaches, and especially to prevent future damage for budget allocation.