Framework for Development and Comprehensive Comparison of Empirical Pavement Performance Models

Abstract

AbstractEmpirical performance-prediction models are a central part of every network-level pavement management system. In this regard, a variety of novel techniques including computational intelligence have been applied, mainly without a systematic approach to ensure compliance with principles of pavement engineering. In this study, a framework is provided for development and comprehensive comparison of alternative techniques for pavement performance modeling. As an example, several machine-learning techniques are compared in developing flexible pavement-roughness prediction models using Federal Highway Administration (FHWA’s) long-term pavement performance (LTPP) data. Three important principles of model development—maximum likelihood, consistency, and parsimony—are considered in providing a robust parameterization guideline. Variant architectures of artificial neural networks (ANN), radial basis function (RBF) networks, and support vector machines (SVM) are tested to determine the optimum parameters. Fin...