Abstract
One of the most common pavement distresses in flexible pavement is rutting, which is mainly caused by heavy wheel load and various other factors. The prediction of rutting depth is important for safe travel and the long-term performance of pavements. Factors that are considered in this paper for the prediction of rut depth are Temperature, Equivalent Single Axle Load, Resilient modulus, and Thickness of hot mixed asphalt. The input data for all factors are collected from the Long-Term Pavement Performance Information Management System for the state of Texas. Regression analysis is performed for dependent and independent variables to obtain the empirical relationship. In various fields of civil engineering, artificial neural networks have recently been utilized to model the qualities and behavior of materials and to determine the complicated relationship between various properties. An Artificial Neural Network is used to develop a predictive model to predict the rutting depth. A total number of 70 observations were considered for the predictive model. A mathematical relation is developed and verified between rut depth and variable input data. © 2023 by the authors.
Description
Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/).
Publisher
MDPI
Date of publication
2-10-2023
Language
english
Persistent identifier
http://hdl.handle.net/10950/4967
Document Type
Article
Recommended Citation
Khalifah, Rami; Souliman, Mena I.; and Bin Mukarram Bajusair, Mawiya, "Development of Prediction Model for Rutting Depth Using Artificial Neural Network" (2023). Civil Engineering Faculty Publications and Presentations. Paper 29.
http://hdl.handle.net/10950/4967