Large Scale Pavement Crack Evaluation Through a Novel Spatial Machine Learning Approach Considering Geocomplexity

Road transport infrastructure is a crucial component of the entire infrastructural network. Timely and efficient maintenance of roads requires accurate and effective evaluation of pavement health, of which cracking is an important aspect. However, accurately assessing pavement cracks across large-scale road networks remains challenging due to spatial variations, which diminish the effectiveness of traditional machine learning methods. This study developed a novel spatial machine learning (SML) model and employed laser scanning data and satellite remote sensing images to assess road segment-based crack severity across the state-level road network in the Wheatbelt of Western Australia. Geocomplexity is introduced to measure the complexity of local patterns and spatial dependence among neighboring road segments. Results showed that SML can accurately and effectively predict pavement cracks on a large spatial scale with an accuracy (AC) from 0.524 to 0.701. In the SMLs, laser-scanning pavement variables contributed 34.15% to 43.33% of the total explainable variations, and geocomplexity variables also contributed significantly, ranging from 27.35% to 49.92%. The SML model exhibited the highest coefficient of determination ( $R^{2}$ ) and AC for crack prediction compared with Multiple Linear Regression (MLR), Generalized Additive Model (GAM), Bayesian Regularized Neural Network (BRNN) and Support Vector Regression (SVR). The findings provided a deep insight into large-scale crack deterioration by considering the spatial characteristics and achieved high-resolution crack assessment to support road maintenance decision-making. The spatial machine learning approach and the concept of geocomplexity can be widely applied to address large-scale spatial tasks in road engineering.