Computationally-Affordable Unsupervised Machine Learning Algorithm to Identify the Level of Distress Severity in Pavement Functional Performance

Abstract

Pavements continually experience various types of distresses that are manifested in the form of defects that will eventually lead to the pavement’s failure. Selection of appropriate preventive maintenance strategies in terms of timing and method retards the large backlog of poor conditions of pavements, therefore improves the system-wide performance in an efficient and cost-beneficial way. However, the pre-treatment functional performance condition of asphalt pavement has a significant impact on the efficacy of preventive maintenance, and to provide years of service, it is necessary to designate an appropriate preventive maintenance method according to the actual functional condition of pavement. This study presents a new unsupervised learning approach for 1) extracting high correlation factors to reduce the dimensionality of feature distress data, and 2) accurately separating attributes that define pavement functional performance into severity levels to effectively assess the practiced preventive maintenance. Three unsupervised machine learning algorithms including feature extract, relevant factor analysis, and cluster analysis were executed. Based on the severity level results of the pre-treated pavement, the effects of four treatment methods (thin overlay, slurry seal, chip seal, and crack seal) were evaluated by a statistical analysis. The preventive maintenance data from the Specific Pavement Studies-3 (SPS-3) experiment of the Long-Term Pavement Performance (LTPP) database are used for the analysis. The results demonstrated that selecting the appropriate maintenance method for the functional performance risk level of the pavement is beneficial in achieving successful treatment results.