LTPP data-based investigation on asphalt pavement performance using geospatial hot spot analysis and decision tree models

Abstract

Environmental factors and truck traffic loads have significant impacts on asphalt pavement performance. This study implements geospatial hot spot, correlation, and decision tree analyses to investigate the impacts of environmental factors and truck traffic loads on asphalt pavement performance. A pavement database with 1725 asphalt pavement sections from the Long-Term Pavement Performance (LTPP) program was built and analyzed using geospatial hot spot analysis to characterize the spatial patterns of environmental factors, truck traffic loads, and asphalt pavement distresses in different climatic regions across the United States and Canada. The statistical correlation analysis was conducted to identify significant correlations among hot spots of environmental factors, truck traffic loads, and asphalt pavement distresses. The decision tree model, which is a supervised machine learning method, was used to assess pavement performance in an area that is associated with higher risks of distress based on contributing environmental and traffic conditions. The hot spot analysis showed that the pavement sections located in the dry no-freeze region had higher percentages of hot spots of truck traffic loads and associated load-induced distresses, such as fatigue cracking, longitudinal wheel path cracking, and rutting. In the dry no-freeze region, higher percentages of pavement sections were also classified as hot spots of transverse cracking. The pavement sections in the wet freeze region are more likely to experience longitudinal non-wheel path cracking and surface roughness. The decision tree models were built to identify the likeliness of hot spots of asphalt pavement distresses using environmental factors and truck traffic loads. These decision tree models provide enhanced decision-making information in pavement design and maintenance.