Probabilistic Life-Cycle Cost Analysis of Pavements

Abstract

Significant research has been conducted over the past decade to enhance probabilistic pavement life-cycle cost analysis (LCCA) models, yet the drawing of broad conclusions from different studies is difficult because of the significant variation in scope and sources of uncertainty. Specifically, these two issues make it difficult to infer from existing research (a) which parameters are significant contributors to uncertainty for a specific context and (b) how context affects the analysis. The goal of this research was to address this problem by implementing an LCCA model in a range of scenarios that vary in location, traffic conditions, design life (e.g., year to first rehabilitation), analysis period, maintenance schedule, and discount rate. Results from the analysis indicated that, in relation to the drivers of variation, uncertainty about initial cost was the principal driver of variation across the case studies. Other parameters, such as the predicted performance of pavement over time, could also be important drivers of variation and in particular a matter for lower-volume roads, for which thinner pavement designs are used. In terms of contextual decisions, some decisions, such as whether to use mechanistic–empirical pavement designs instead of the paving design manual of a state department of transportation to determine future maintenance events, seem to have a larger impact than do others. For example, analysis period and design life, though important, affect the final results significantly less, although in some instances they can play a role in differentiating between alternative designs.