Abstract
Many jointed plain concrete pavements (JPCP) on critical roads in the United States are aged and have reached the end of their design lives. They thus require maintenance, rehabilitation, and reconstruction (MR&R) actions, which mainly involve slab replacement or lane reconstruction. Limited budgets challenge transportation agencies to determine the most cost-effective MR&R strategies, especially when life-cycle cost analysis (LCCA) is limited by the unreliable prediction of the pavement’s future needs. This paper proposes an enhanced LCCA-based methodology that utilizes slab-based cracking data collected using 3D laser technology, to select the best strategy for MR&R of JPCP by determining the timing and cost of slab replacement and lane reconstruction. By predicting pavement performance based on the current slab-based condition state using a Markov chain forecasting model, slab replacement projects are scheduled, and their feasibility is evaluated to determine the proper timing for lane reconstruction within the analysis period. LCCA is then conducted to select the alternative with the most cost-effective strategy for scheduling slab replacement and lane reconstruction projects. A case study is conducted on two 1-mi segments of I-16 in Georgia to validate the proposed methodology, followed by a sensitivity analysis to identify the input variables having a significant impact on the LCCA results. The developed framework proved its strength in determining the best MR&R strategy based on segment-level need assessment, which is utilized to perform “what if” analyses that evaluate different scenarios of project scheduling and accommodate the requirements and limitations defined by transportation agencies.
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