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States’ Departments of Transportation (DOT) are trying to utilize the best practices of managing low-volume roads (LVRs) due to limited resources and declined transportation funding. Diverse maintenance practices and fluctuating budget allocations are noticed on LVRs which significantly impact the overall pavement performance. In this study, the optimal scheduling of maintenance strategies and effectiveness of different maintenance policies are investigated. First, the accumulated field experience of Colorado DOT’s pavement engineers is highlighted through a regional survey of practice. Then, multi-year optimization models were developed using genetic algorithms with different objective functions and constraints. Using a case study of LVRs in Region 4, these models were able to study the effectiveness of considering in-place pavement recycling on roads with poor conditions compared to the applications of only thin overlays and chip seals. They also defined the benefit-cost impact of raising the overall drivability of pavement through different maintenance scenarios. In addition, the statewide analysis shows the effectiveness of current maintenance resources on future pavement conditions and defines the budget needs of each scenario. Moreover, an effective decision-making process is achieved for each Colorado DOT’s engineering region using a machine-learning approach. Multiple treatment alternatives are proposed using artificial neural networks with pattern recognition algorithms.

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