Modeling of asphalt pavement performance indices in different climate regions using soft computing techniques

• Main Author: Ali, Abdualmtalab Abdualaziz Yeklef
• Format: Thesis
• Language: English
• Published: Memorial University of Newfoundland 2022
• Subjects: Canada; Newfoundland
• Online Access: https://research.library.mun.ca/15883/; https://research.library.mun.ca/15883/1/converted.pdf

Pavement Management Systems (PMS) enhance pavement performance over the pavements' predicted lifespan by maximizing pavement life. PMS have become an essential aspect of construction and maintenance in the road domain, providing significant cost and energy emission reductions. In addition, using pavement performance prediction models have become an important part of PMS as a technically method for road engineers and various transportation agencies during the past several decades. The Pavement Condition Index (PCI) and International Roughness Index (IRI) are generally accepted methods for gauging ride quality and pavement conditions. Asphalt pavements are highly sensitive to various parameters, including pavement distress, environment, and traffic volume. Hence, studying these variables while developing prediction models is a vital step that can help develop asphalt pavement performance indices. This research aimed to introduce an effective method for developing asphalt pavement performance indices in different climate regions. This research provided a methodology to develop performance models using three soft computing techniques, namely the fuzzy inference system (FIS), multiple linear regression (MLR), and artificial neural networks (ANNs). Two sources were used for the extracted dataset: the long-term pavement performance (LTPP) data set for four climate regions in the U.S. and Canada and filed survey data of section roads of St. John's, Newfoundland, Canada. First, for the classification section, the research presented in this study provided a FIS that uses appropriate membership functions for computing PCI and IRI values. A fuzzy input was calculated by considering the degree of distress from nine density types of pavement distress coefficients (rutting, fatigue cracking, block cracking, longitudinal cracking, transverse cracking, patching, potholes, bleeding, and ravelling), which were considered as fuzzy input variables. Results presented that the rutting and transverse cracking had the most significant ...