An ANN-Based Approach for Nondestructive Asphalt Road Density Measurement

Abstract

Asphalt pavement’s density measurement is an important step in the quality control of asphalt road construction. It is usually achieved by applying the coring method (CM), nuclear density gauge (NDG), and electromagnetic density gauge (EDG). CM is the most accurate method, but it is a destructive method because the pavement is damaged when the cores are taken. NDG and EDG are nondestructive methods with high efficiency, but their measurement accuracy is poorer than that of CM. An EDG commonly used in density measurement is named pavement quality indicator (PQI). A novel method named density profiling system (DPS) is also based on the potential EDG. However, it was not applied to this research because more tests are required to verify its accuracy. This paper presents an approach to improve the accuracy of the nondestructive methods with NDG and PQI. It is based on the artificial neural network (ANN), which processes the raw data got from NDG and PQI and produces the predicted asphalt density as the output. The density measured in CM was used as the target density and the error between ANN-predicted density and target density was computed. To minimize this error, various ANN architectures and learning algorithms were tried in the ANN training process. Each established ANN model makes a substantial improvement in the performance of NDG or PQI in asphalt density measurement.