Probabilistic Characterization of Uncertain Inputs in the Life-Cycle Cost Analysis of Pavements

Abstract

Life-cycle cost analysis (LCCA) is an important tool for evaluating the merits of alternative investments. Inevitably, inputs for an LCCA are subject to a high level of uncertainty in both the short and long term. However, the way LCCA is currently implemented in the field treats LCCA inputs as static, deterministic values. Conducting an analysis in this way, though computationally simpler, hides the underlying uncertainty of the inputs by only considering a few possible permutations. The results of such an analysis could potentially lead a decision maker to the incorrect pavement selection. One methodology that has gained traction in the past decade is describing uncertain parameters probabilistically and allowing the analysis to consider a range of possible outcomes. Although this methodology is recommended by FHWA, practitioners still generally conduct deterministic LCCAs. One of the major reasons is that further work must be conducted to characterize uncertainty statistically for input parameters. This research attempted to build on previous work by probabilistically characterizing several input parameters for which empirical data were and were not readily available. This paper characterizes uncertainty and variability in the LCCA of pavements and then applies the methodology presented to two case studies to understand the implications of a probabilistic LCCA for the pavement selection process.