Fuzzy Logic Pavement Maintenance and Rehabilitation Triggering Approach for Probabilistic Life-Cycle Cost Analysis

Abstract

Life-cycle cost analysis (LCCA) is an important tool in transportation asset management. Transportation agencies have used both deterministic and probabilistic LCCA approaches. Probabilistic approaches allow decision makers to evaluate the risk of an investment by using uncertain input variables, assumptions, or estimates. The incorporation of fuzzy logic–based models into the risk analysis process is explored to enhance further the traditional probabilistic LCCA method. It proposes a fuzzy logic approach for determining the timing of pavement maintenance, rehabilitation, and reconstruction (MR&R) treatments in a probabilistic LCCA model for selecting pavement MR&R strategies. Instead of using predefined service life for initial construction and future rehabilitations, the proposed approach uses performance curves and fuzzy logic triggering models to determine the most effective timing of MR&R activities. The new approach is compared with the deterministic and traditional probabilistic approaches in a simple case study. The case study demonstrates that the fuzzy logic–based risk analysis model for LCCA can effectively produce results that are at least comparable to those of the benchmark methods while effectively considering some of the uncertainty inherent to the process.