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Since its inception in 2000, Mississippi’s pavement warranty programme has been used to manage the maintenance activities of the warranty pavement projects. To determine whether a specific maintenance treatment is required on a warranty pavement section, distress measurements are taken and compared against warranty distress thresholds for each distress type. Then, the decisions of what maintenance treatments and scopes should be applied are made by following the warranty specifications. This study aims to identify factors that influence past maintenance decisions and predict future maintenance needs for warranty projects based on past decisions. Statistical methods were employed to identify the relationship between the maintenance decisions and relevant factors based on the historical data of the warranty projects. Moreover, logistic regressions were performed to develop a comprehensive maintenance decision-making prediction model for the warranty pavements. The analysis results showed that the distress measurements at low and medium severity levels in numerical variables and project location and distress type in categorical variables have stronger effects than the others on decisions. The mixed-effects logistic regression model could provide a high accuracy in predicting the remedial actions, which could further provide a trend prediction for the maintenance decision of a warranty pavement section.

Factor analysis of maintenance decisions for warranty pavement projects using mixed-effects logistic regression