A Study of Factors Affecting GPR Signal Amplitudes in Reinforced Structures Using Deep Belief Networks

Abstract

The applications of the deep belief network (DBN) for addressing practical engineering issues have recently emerged all over the world thanks to its accuracy and availability of data. In this paper, a predictive model using DBN was employed to investigate the factors that affect the ground-penetrating radar (GPR) signals from the rebar embedded in concrete structures. Four variables, namely temperature, relative humidity, chloride contamination level, and rebar surface corrosion condition were used as the model inputs for the investigation. Comprehensive data acquired from previously published documents were used to establish the proposed DBN model. It was shown that temperature and chloride contamination level variables generated significant effects on the GPR amplitude signal from rebar. In contrast, the relative humidity and rebar surface corrosion condition parameters were found to yield a minimal influence on the output of the proposed model. The DBN model can be used to predict the amplitude of GPR signals from the four inputs with a high level of accuracy. Specifically, the coefficient of determination (R2) was 0.9634 and 0.9681 for the testing dataset and the entire database, respectively.