An Artificial Neural Network-Based Approach to Improve Non-Destructive Asphalt Pavement Density Measurement with an Electrical Density Gauge

Abstract

Asphalt pavement density can be measured using either a destructive or a non-destructive method. The destructive method offers high measurement accuracy but causes damage to the pavement and is inefficient. In contrast, the non-destructive method is highly efficient without damaging the pavement, but its accuracy is not as good as that of the destructive method. Among the devices for non-destructive measurement, the nuclear density gauge (NDG) is the most accurate, but radiation in the device is a serious hazard. The electrical density gauge (EDG), while safer and more convenient to use, is affected by the factors other than density, such as temperature and moisture of the environment. To enhance its accuracy by minimizing or eliminating those non-density factors, an original approach based on artificial neural networks (ANNs) is proposed. Density readings, temperature, and moisture obtained by the EDG are the inputs, and the corresponding densities obtained by the NDG are the outputs to train the ANN models through Levenberg-Marquardt, Bayesian regularization, and Scaled Conjugate Gradient algorithms. Results indicate that the ANN models trained greatly improve the measurement accuracy of the electrical density gauge.