ABSTRACT
Existing pavement management systems (PMSs) typically aggregate short survey sections into long homogeneous segments, often ranging from 1 to 5 kilometres, to reduce data complexity and formulate a more tractable optimization problem. However, the assumption that sections with similar conditions will deteriorate at the same rate is inherently flawed. This leads to imprecise predictions, threshold violations, and suboptimal maintenance and rehabilitation (M&R) decisions. This paper presents a comprehensive framework for a new PMS that eliminates the need for composite condition indices, data aggregation, and homogeneous road sections in the M&R optimization process. The proposed bottom-up approach uses short survey sections (25-50 meters) and optimizes treatment type, timing, work-zone length, and layout to minimize life cycle costs for road agencies, users, and the environment. Rather than relying on a single condition indicator, the model simulates the effects of multiple pavement distress types and their combinations, providing a more robust and nuanced evaluation of pavement conditions. The short survey sections are grouped into larger work zones based solely on economic considerations, such as economies of scale, ensuring cost-efficient operations. Treatment costs, temporary traffic control costs, and work-zone user costs are modelled as functions of project size and are calibrated using actual cost data from Nigeria. Additionally, compaction test models are employed to predict pavement performance and calculate expected deterioration rates over time. To handle the optimization of a potentially unlimited number of sections, genetic algorithms are incorporated, allowing scalability for large road networks. Furthermore, the framework can account for budget restrictions, risk assessments, and cross-asset fund allocation, making it suitable for any aging or repairable infrastructure system.
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