Modeling the pavement serviceability ratio of flexible highway pavements by artificial neural networks

• Published in: Construction & building materials Vol. 21; no. 3; pp. 590 - 593
• Main Author: Terzi, Serdal
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2007
• Subjects: Artificial neural networks; Flexible highway pavements; Pavement present serviceability index; Pavement present serviceability index; Flexible highway pavements; Artificial neural networks
• ISSN: 0950-0618; 1879-0526
• DOI: 10.1016/j.conbuildmat.2005.11.001

The term “present serviceability” was adopted to represent the momentary ability of pavement to serve traffic, and the performance of the pavement was represented by its serviceability history in conjunction with its load application history. Serviceability was found to be influenced by longitudinal and transverse profile as well as the extent of cracking and patching. The amount of weight to assign to each element in the determination of the over-all serviceability is a matter of subjective opinion. In this study, artificial neural networks (ANN) is used in modeling the present serviceability index of the flexible pavements. Experimental data obtained from AASHTO include slope variance, rut depth, patches, cracking and longitudinal cracking. The developed ANN model has higher regression value than AASHO model. This approach can be easily and realistically performed to solve the problems which do not have a formulation or function about the solution.