Title: Artificial Neural Network Utilization for Nondestructive Testing and Evaluation of Concrete Structures

Author(s): Wael A. Zatar, M. Ammar Alzarrad, Tu T. Nguyen, and Hai D. Nguyen

Publication: Symposium Paper

Volume: 350

Issue:

Appears on pages(s): 167-177

Keywords: artificial neural network; concrete imaging; evaluation; ground penetrating radar; nondestructive testing

DOI: 10.14359/51734322

Date: 11/1/2021

Abstract:
In this paper, the artificial neural network (ANN) method is utilized to predict ground-penetrating radar reflection amplitudes from four different inputs, namely, temperature, ambient relative humidity, chloride level, and corrosion condition on the surface of the reinforcing bar. A total of 288 ground penetrating radar (GPR) data points were collected from a series of chloride-contaminated concrete slabs under various environmental profiles that were used to train, validate, and test the proposed ANN model. The ANN model performed well in predicting the GPR reflection signals, with the overall coefficient of determination (R2) being 0.9958. The overall mean squared error (MSE) and root mean squared error (RSME) values are 0.015 and 0.122, respectively. These values are very low, which means that the ANN model has an excellent prediction capability. The research results show that the GPR reflection amplitudes are more sensitive to the temperature changes and chloride level parameters than the ambient relative humidity and rust condition on the reinforcing bar surface. Using the ANN method to predict the GPR reflection amplitudes is relatively new for structural concrete applications. This study paves the way for further developments of neural networks in civil and structural engineering.