Pavement Condition Assessment Using Fuzzy Logic Theory and Analytic Hierarchy Process

Abstract

This paper integrates the advantages of the analytic hierarchy process (AHP) and fuzzy logic theory and develops a new approach for pavement condition assessment and project prioritization. Roughness, deflection, surface deterioration, rutting, and skid resistance are identified as five performance indicators for evaluating pavement condition. A survey is conducted among experienced professional engineers for establishing fuzzy membership functions of each performance indicator with respect to a fuzzy linguistic evaluation set Very good, Good, Fair, Poor, and Very poor using statistical regression. AHP is applied to determine weight from a paired-comparison matrix. Eventually the fuzzy comprehensive evaluation is carried out using fuzzy relations, which combines a fuzzy evaluation of single performance indicators to the one simultaneously considering all five performance indicators. A maximum grade principle (MGP) and a defuzzified weighted cumulative index (DWCI) are proposed to assign a linguistic assessment result and a numerical assessment result, respectively, to the condition of a road segment. A case study is provided to rank eight road segments using MGP and DWCI. The proposed method offers a promising approach to the accuracy and reliability issues in the data collection and rating of pavement distress condition.