Application of Artificial Neural Networks to Concrete Pavement Joint Evaluation

Abstract

Application of the principles of dimensional analysis has recently led to the development of a robust method for assessing the deflection and stress load transfer efficiencies of concrete pavement joints and for backcalculating joint parameters. The new method eliminates the need to make a priori assumptions since pertinent inputs can now be experimentally determined using the falling weight deflectometer. A data base has been generated using numerical integration of Westergaard-type integrals and has been used to train a backpropagation neural network algorithm for joint evaluation. The resulting computer program is simple, efficient, and precise and can be used on site for immediate results. Its predictions are verified by comparisons with closed-form and finite-element solutions pertaining to data collected at three major civilian airports in the United States, including the new Denver International Airport. Also discussed is the role of dimensional analysis in the generation of the training set for a neural network. It is demonstrated that significant savings can be achieved through reduction of the dimensionality of the problem, which could be reinvested in broadening the range of applicability of the neural network. Comparison of neural network predictions with those from conventional regression analysis and from direct interpolation illustrates the benefits of data generation on the basis of fundamental principles of mechanics.