A Comparative Analysis of Two Artificial Neural Networks Using Pavement Performance Prediction: Fid-Bau Portal

A Comparative Analysis of Two Artificial Neural Networks Using Pavement Performance Prediction

Roberts, Craig A. / Attoh‐Okine, Nii O.

The management of pavements requires the ongoing allocation of substantial manpower and capital resources by the responsible agencies. These agencies ultimately report to the executive and legislative branches of government, which require justification and proof of the efficacy of these expenditures. This and the need for improved engineering technical feedback have encouraged the development of pavement management systems (PMS). One goal of a PMS is to provide decision makers at all levels with optimal resource‐allocation strategies. This requires evaluation of alternatives over an analysis period based on predicted values of pavement performance. This necessitates more reliable pavement performance prediction models. Traditional modeling uses multiple regression techniques to predict pavement performance from traffic, time, and pavement distress or various combinations of these factors. Within the last 10 years, new modeling techniques, including artificial neural networks (ANNs), have been applied to transportation problems. The ANNs examined usually have been of a single type called a dot product ANN. This paper examines a different type called the quadratic function ANN and compares the results to the dot product ANN. The quadratic function ANN is a generalized adaptive, feedforward neural network that combines supervised and self‐organizing learning. Models were developed to predict roughness using both types of ANN on the same data samples and the results compared. The data samples were drawn from the Kansas Department of Transportation's PMS database. The results indicate a significant improvement in the use of the self‐organizing quadratic function ANNs and lead to recommendations for specific areas of additional research.