Prediction of Degradation of Concrete Surface Layer Using Neural Networks Applied to Ultrasound Propagation Signals

The aim of this article is the development of a new artificial intelligence (AI) system for the condition assessment of concrete structures. To study the process of concrete degradation, the so-called spatiotemporal waveform profiles were obtained, which are sets of ultrasonic signals acquired by stepwise surface profiling of the concrete surface. The recorded signals at three frequencies, 50, 100 and 200 kHz, were analyzed and informative areas of the signals were identified. The type of the created neural network is a multilayer perceptron. Stochastic gradient descent was chosen as the learning algorithm. Measurement datasets (test, training and validation) were created to determine two factors of interest—the degree of material degradation (three gradations of material weakening) and the thickness (depth) of the degraded layer varied gradually from 3 to 40 mm from the surface. This article proves that the training datasets were sufficient to obtain acceptable results. The built networks correctly predicted the degree of degradation for all elements of the test dataset. The relative error in prediction of a thickness of degraded layer did not exceed 3% in the case of a thickness of 25 mm. It is shown that the results for the Fourier amplitude spectra are significantly worse than the results of neural networks built based on information about the measured signals themselves.