Predicting pavement condition index based on the utilization of machine learning techniques: A case study

Abstract

Pavement management systems (PMS) are used by transportation government agencies to promote sustainable development and to keep road pavement conditions above the minimum performance levels at a reasonable cost. To accomplish this objective, the pavement condition is monitored to predict deterioration and determine the need for maintenance or rehabilitation at the appropriate time. The pavement condition index (PCI) is a commonly used metric to evaluate the pavement's performance. This research aims to create and evaluate prediction models for PCI values using multiple linear regression (MLR), artificial neural networks (ANN), and fuzzy logic inference (FIS) models for flexible pavement sections. The authors collected field data spans for 2018 and 2021. Eight pavement distress factors were considered inputs for predicting PCI values, such as rutting, fatigue cracking, block cracking, longitudinal cracking, transverse cracking, patching, potholes, and delamination. This study evaluates the performance of the three techniques based on the coefficient of determination, root mean squared error (RMSE), and mean absolute error (MAE). The results show that the R2 values of the ANN models increased by 51.32%, 2.02%, 36.55%, and 3.02% compared to MLR and FIS (2018 and 2021). The error in the PCI values predicted by the ANN model was significantly lower than the errors in the prediction by the FIS and MLR models.