Maher Mahmood, Mujib Rahman, Senthan Mathavan, Lars Nolle
الملخص العربي
Pavement section classification is one of the key elements of the decision-making process in a pavement man-agement system. It helps to monitor the pavement conditions and assists in the optimization of maintenance and rehabilitation requirements. This paper presents a fuzzy inference system (FIS), with appropriate member-ship functions, for section classifications and for calculating the Pavement Condition Index (PCI). The FIS is a powerful tool to deal with the uncertainty and subjectivity involved in section classification. The input data of FIS were obtained from the Long-Term Pavement Performance (LTPP) database. The severity and extent of seven distress types, namely alligator cracking, block cracking, longitudinal and transverse cracking, patch-ing, potholes, bleeding, and raveling, were used for fuzzy membership function and rule generation. The out-put fuzzified PCI was compared with the PCI calculated by Micro-PAVER. The result shows a correlation of approximately 76% between the two methods. A sensitivity analysis was carried out to evaluate the effect of each distress type on the classification model. It was found that within the tested sections, a pavement crack has the greatest influence on section classification compared to the other distress types.
تاريخ النشر
10/06/2015
الناشر
International Conference on Bituminous Mixtures and Pavements, 6th, 2015, Thessaloniki, Greece
ماهر شاكر محمود | MAHER SHAKIR MAHMOOD | 3427