Large-scale Pavement Crack Evaluation and Prediction using a Novel Spatial Machine Learning Approach

Abstract

This study introduces a geocomplexity-enhanced machine learning (GML) model that integrates spatial methodologies to uncover influencing factors of crack severity obtained from human inspection and laser scanning. These two aspects, representing existing surface crack condition, are then integrated with a risk of deterioration to develop a comprehensive crack evaluation framework.