Search

Data quality assurance (QA) procedures have been developed and implemented to evaluate the quality of automated pavement distress data. Most State Departments of Transportation (DOTs) use fixed acceptance thresholds to evaluate data quality at the network level and project level. However, errors of automated measurements can be affected by many factors such as pavement types and pavement conditions. It is necessary to take those factors into account while developing the QA acceptance thresholds. The objective of this research is to propose thresholding methods that could effectively utilise these factors to improve QA of the automated data. In this study, the potential problems associated with the use of fixed acceptance thresholds for QA were first discussed. Then, one conventional prediction interval method and one bootstrapping prediction interval method were proposed for QA procedures. The proposed prediction interval based methods can be used to determine variable thresholds that are sensitive to measurement errors. Lastly, QA results from the proposed methods and a fixed thresholding method were compared and analysed. The experimental results showed that prediction interval methods were superior to the fixed threshold methods in identifying pavement sections with data quality issues and evaluating the overall data quality of the network. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]