Influence of Pavement Condition Data Variability on Network-Level Maintenance Decision

Abstract

This study evaluated the impacts of data variability on pavement performance evaluation and maintenance planning. Three methods were used to quantify the variability of data. For pavement roughness and rut depth, the ratio of the difference between two wheelpaths over the sum value was used to quantify the data variation. For distress data, Monte Carlo simulation was used to quantify the error in distress extent, and the transition matrices were used to quantify the error in distress severity classification. An evaluation framework was proposed to investigate the influence of data variability on maintenance planning. The analysis results indicate that roughness for state routes exhibited a larger variation than that for Interstates. Pavement surface on the left wheelpath generally was smoother than that on the right path, regardless of route type. In regard to distress severity, the accuracy of distress extent at a low severity level had little influence on the overall distress index, while the accuracy of distress extent at moderate and high severity levels significantly influenced the overall distress index. The accuracy of distress severity at a moderate level had the most significant influence on the pavement distress index. For the current pavement management system used in Tennessee, the variability of roughness and distress severity level were the dominant influencing factors for maintenance planning, whereas the variability of distress extent had a slight influence on maintenance planning. There was no significant influence of variability of rut depth on maintenance planning.