Browsing by Author "Banerjee, Ambarish"

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    Developing Optimized Maintenance Work Programs for an Urban Roadway Network using Pavement Management System
    Beg, Muhammad A.; Banerjee, Ambarish (2015-06-04)
    Pavement management systems (PMS) are used by highway agencies to establish best possible network level maintenance and rehabilitation work programs for the road network. PMS also help in establishing the funding levels required to meet agency desired pavement performance or level of service goals. Pavement management involves the identification of optimum maintenance strategies at various management levels. It aims to determine the most efficient maintenance program that yields maximum benefit for the public funds expended. However, the PMS generated maintenance programs can vary significantly based on underlying variables, decision trees, models and overall rationale exercised by pavement management engineers. Producing a network level work program that applies the right treatment at the right time on each network section is central to the success of a pavement management program. Uncertainties and constraints imposed on the maintenance funding levels underscore the need for implementing optimal maintenance programs. Having more accurate knowledge about the funding levels for future years, reliable pavement condition models, and reliable engineering inputs, can help an agency adopt an aggressive and efficient pavement preservation program that will extend the useful life of the pavement network. This paper evaluates a variety of pavement work program scenarios for an urban road network which typically have large number of routes, most of them short in length and consequently have large number of short management sections in the network. Moreover, engineers need to account for network aberrations like intersections, turning lanes, curbs & gutter and similar features. This paper discusses several network analyses options available for establishing optimized work programs for urban road networks and outlines their individual strengths over other analysis methodologies.