Machine Learning for Pavement Performance Modelling in Warm Climate Regions

Accurate pavement performance modelling is an essential requirement for cost-effective pavement design and enhances pavement management decision making. Due to the complexity of the pavement structure and pavement response, dominant failure mechanisms may vary depending on the climate region: cold or warm. This study investigated the significance of pavement design factors on pavement performance in warm regions and compared them to set of factors previously identified for cold regions. An artificial neural network (ANN) supported by a forward sequential feature selection algorithm was employed to identify the most significant design factors prevailing in warm climate regions using data extracted from the Long-Term Pavement Performance database. In addition, five machine learning techniques were utilized to model the pavement performance in warm regions, namely: regression tree, support vector machine, ensembles, Gaussian process regression, and ANN. Moreover, conventional regression modelling was used for comparison assessment. The analysis revealed seven design factors that are significantly impacting asphalt pavement performance in warm regions: initial roughness, relative humidity, average wind velocity, average albedo, average emissivity, traffic volume, and pavement structural capacity. The results indicate that pavement performance in warm climate regions is dominated by different environmental factors than those found for cold climate regions. The ANN modelling technique produced the most accurate asphalt pavement performance models.