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.
With the increasing adoption of three-dimensional (3D) pavement imaging systems by highway agencies for automated pavement condition assessment, coupled with the advent of diverse systems from different manufacturers, there is a need for standard procedures for the verification and certification of systems’ performance in regard to distress identification, especially for cracking that is a key contributor for triggering maintenance and rehabilitation activities. Although some procedures were adopted by agencies for a rough verification of cracking identification accuracy using ground reference established subjectively by trained raters, a more rigorous and objective method is needed to match the continuous advancement in the systems’ capabilities and data quality requirements. As portable high-resolution 3D scanning technologies have become commercially available, there is an opportunity to leverage them for establishing a more trustable ground reference for the data quality evaluation. This paper proposes a methodology that uses high-resolution 3D scanners to establish the ground reference for field pavement cracking distress to evaluate the crack identification capability of 3D pavement imaging systems in regard to crack quantity, position, and width. A case study was performed by scanning sample pavement cracking spots using “FARO Arm Quantum S” scanner to collect ground reference images and a 3D pavement imaging system installed on the “Georgia Tech Sensing Van” to collect test images to validate the feasibility of the proposed methodology.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.