Life-cycle cost analysis (LCCA) has grown in importance, yet it is also regarded as a daunting task, because of the lack of reliable analytical models specifically aimed at quantifying future maintenance costs (FMC). The key objectives of this study are to identify the most critical factors that affect pavement performance, create a FMC predictive model that accounts for such factors, and validate the model’s robustness in quantifying a reliable FMC for low-volume highway rehabilitation projects. This study combines regression analysis techniques with a cluster analysis by using a large quantity of real-world data obtained from the pavement management information system. The clustering analysis revealed that traffic loading would be the most crucial affecting factor for pavement performance. A series of sensitivity analyses were performed to investigate the effect of individual critical performance-driven factors on FMC. A prediction error analysis validated the model’s robustness, proving that reliable FMC can be estimated by analyzing how it interacts with the affecting factors. This study assists industry practitioners and researchers in quickly and reliably determining long-term FMC for incorporation into LCCA. Knowing a conceptual estimate of FMC in the very early project scoping stage could also help them develop a sounder strategic plan within project constraints and peculiarities from the LCCA standpoint.
|Journal||Journal of Computing in Civil Engineering|
|Early online date||11 Sep 2015|
|Publication status||Published - 1 Jul 2016|