Analysis of learning rate and momentum term in backpropagation neural network algorithm trained to predict pavement performance

Abstract

Pavement performance modeling is a critical component of any pavement management system (PMS) decision-making process. A characteristic feature of pavement performance models is that they are formulated and estimated statistically from field data. The statistical modeling can only consider no more than a few of the parameters, in a simplified manner, and in some cases various transformations of the original data. Lately, artificial neural networks (ANNs) were applied to pavement performance modeling. The ANNs offer a number of advantages over the traditional statistical methods, caused by their generalization, massive parallelism and ability to offer real time solutions. Unfortunately, in pavement performance modeling, only simulated data were used in ANNs environment. In this paper, real pavement condition and traffic data and specific architecture are used to investigate the effect of learning rate and momentum term on back-propagation algorithm neural network trained to predict flexible pavement performance. On the basis of the analysis it is concluded that an extremely low learning rate around 0.001–0.005 combination and momentum term between 0.5–0.9 do not give satisfactory results for the specific data set and the architecture used. It is also established that the learning rate and momentum term, and validation data can be used to identify when over-learning is taking place in a training set.