Pavement Condition

Abstract

The collection of pavement condition data is one of the most important and costly elements of operating a pavement management system (PMS). This function is crucial, as business decisions rely on it: a PMS should be able to prioritize maintenance, rehabilitation, and reconstruction effectively. Pavement condition data are usually transformed into a numerical rating system [a pavement condition index (PCI)] that qualitatively describes individual pavement segments or a network. The Iowa Department of Transportation's PCI is calculated by using PCI equations that are based on statistical regression analysis. Different attributes are used for different pavement families. The study summarized here aimed to develop a new condition index that provides a consistent, unified approach to rating pavements in Iowa. The proposed system has a 100-point scale that is based on five indexes derived from specific distress data or pavement properties and an overall index that combines individual indexes with weighting factors. The indexes cover cracking, ride, rutting, faulting, and friction. The cracking index is formed by combining cracking data (i.e., transverse cracking, longitudinal cracking, wheelpath cracking, and alligator cracking indexes). Ride, rutting, and faulting indexes use the international roughness index, rut depth, and fault height, respectively. The proposed overall PCI is made up of 40% cracking index, 40% ride index, and 20% faulting index for portland cement concrete pavements and 40% cracking index, 40% ride index, and 20% rutting index for asphalt concrete pavements. The proposed condition index was compared with the current PCI and, in general, was found to offer fairly good correlation.

Prediction of Pavement Overall Condition Index Based on Wrapper Feature-Selection Techniques Using Municipal Pavement Data

“…All this information is used to update the street-segment data in geographic information system (GIS) format. More information is to be found in Bou-Saab et al ( 1) and Bektas et al (34). The total number of raw data records was 8321, of which 2172 were for composite-pavement data, 5941 for concrete, and the rest for other pavement types.…”

“…The data collection in this research is fully automated by an Automated Road Analyzer (ARAN) van. The ARAN van collects and stores digitized video images of the pavement surface and processes them using pattern recognition with manual oversight to identify, quantify, and classify the types of distresses ( 34 ). Pavement distress is collected in one direction on two-lane highways or in two directions if there is a median.…”

“…Examples of such indices are the Pavement Serviceability Index (PSI), Pavement Serviceability Ratio (PSR), Pavement Condition Rating (PCR), Ride Number (RN), Profile Index (PI), International Roughness Index (IRI), Pavement Condition Index (PCI), and the Overall Condition Index (OCI). The OCI is a numerical indicator utilized by North American transportation agencies like the Iowa Department of Transportation (Iowa DOT) ( 5 ). The OCI uses the pavement surface evaluation and rating (PASER) manual to identify roadways needing repair ( 1 ).…”

Improving vision-based automated pavement condition data acquisition and assessment through deep learning and data-driven techniques

“…PCI values are used extensively in evaluating maintenance requirements for specific pavement segments and for budgetary planning. The Iowa Department of Transportation spends about $1 million annually on collecting pavement condition data (Bektas et al, 2014). Hot mix asphalt (HMA) and Portland Cement Concrete (PCC) pavement condition can be ascertained using four indices, each of which is scaled using a weighting factor and summed to calculate the overall PCI.…”

“…The Iowa DOT has a two-cycle program to collect pavement condition data on all state routes, with data being collected on a cycle every alternate year. The collected data is used to report the pavement condition index (PCI) values at fixed length interval (Bektas et al, 2019). Figure 1-1 shows Iowa DOT's data collection program on state routes (Smadi & Maze, 1998).…”