A GIS-based early warning tool for pavement deterioration due to unusually heavy truck loads

Abstract

Budget from transportation agencies cannot meet the increasing requirements of pavement maintenance. Preventative pavement maintenance is accepted as a more cost-effective way to keep pavement in an acceptable level with less investment, compared to the reactive pavement maintenance. Traffic information, especially that about heavy truck trips, can help agencies to determine when and how to take preventative pavement maintenance. Two types of unusually heavy truck trips, associated with energy extraction (oil and gas) and urban development activities, were analyzed in this study. Their equivalent single axile loads (ESALs) were then mapped to the network. In addition, existing pavement condition, measuring by ride score, one of the common indexes of Pavement Serviceability, was considered into the model. A new index, ESALs divided by ride score, was introduced to reflect the priority of pavement maintenance of the network influenced by the unusually heavy truck trips. A GIS-Based model, automating the process of analysis by employing the Python Toolbox and online ArcGIS Add-in, was developed. Results include the maps and Excel tables to help agencies understand the priority of pavement maintenance of the network in terms of ESALs and current pavement condition.