Data-Driven Web-Based Patching Management Tool Using Multi-Sensor Pavement Structure Measurements

Abstract

Automating pavement maintenance suggestions systems is challenging, especially for actionable recommendations such as patching location, depth, and priority. It is a common practice among state agencies to manually inspect road segments of interest and decide maintenance requirements based on the pavement condition index (PCI). However, standalone PCI only evaluates the pavement surface condition which, coupled with the variability in human perception of pavement distress, limits the accuracy and quality of current pavement maintenance practices. In this case, there is a need for multi-sensor data integrated with standardized pavement distress condition ratings. This study explores estimating the appropriate pavement patching strategy (i.e., patching location, depth, and quantity) by integrating pavement structural and surface condition assessment with pavement ratings of distress. In particular, it combines pavement structural condition parameters and falling weight deflectometer deflections with surface condition parameters, international roughness index, and cracking density, for a better representation of overall pavement distress conditions. Then, a pavement-specific, threshold-based patching suggestion algorithm is designed to suggest pavement maintenance operations. The thresholds were determined based on a reliability concept and were verified with the structural number ratio. The threshold values were then used in the patching suggestion algorithm to create patching suggestion tables. A web-based Patching Management Tool (PMT) was designed as an interactive tool to visualize these patching suggestion maps and analyze the pavement distress data using geographical maps and graphs. The PMT was validated with road surface and right-of-way images obtained from three-dimensional laser sensors, and it could successfully capture localized distresses in existing pavements.