Abstract

A pavement management system (PMS) is a set of tools or methods that assist decision makers in finding optimum strategies for providing and maintaining pavements in a serviceable condition over a given period of time at the least cost. Without such a routine pavement maintenance program, roads require more frequent reconstruction, thereby costing the state and local governments additional dollars. In this study, a prototype PMS for municipal maintained roads was developed for a typical township in Rhode Island, the Town of South Kingston. Based on the results of a questionnaire survey and comparative analysis, Micro PAVER was selected as the most appropriate microcomputer-based PMS for this particular purpose. A prototype PMS was prepared using the Micro PAVER as the core. Pilot implementation of the Micro PAVER PMS was conducted in the Town of South Kingston. Surface distresses were visually observed to evaluate the pavement condition, and the prioritization was based on the derived pavement condition index (PCI). The developed prototype PMS used a ten percent sampling technique for pavement condition surveys. A preliminary list of techniques and costs for maintenance and rehabilitation (M&R) was prepared, and deterioration rate curves were developed for the selected network. The establishment of a GIS network for the town consisted of developing the following coverage: a town boundary, pavement management zone boundary, study area boundary and a road network in the study area. The coverage were developed using the PC ARC/INFO GIS and the United States Geological Survey (SSGS 7.5 minute series) 1:24,000 digitized maps. The data integration involved bringing together separate software components and reducing database management duplication. The GIS integration process required the PMS data to be in ASCII format. The Micro PAVER management data were converted to R:BASE SYSTEM V and exported as an ASCII fixed file. TABLES database manager under PC ARC/INFO starter kit was used t create templates that formed the attribute data e.g., pavement age, surface type, zone identification, pavement rank, branch number, section number and street name. The pavement condition data were then imported to TABLES and linked to roadway network using the pavement section identification numbers. These ID numbers are common to the digitized road network and the pavement data. Templates were also created for the study area boundary coverage with attributes such as average PCI, number of sections and area of pavements. Once the databases were complete, the data were queried to create maps for

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