The integrity of the internal structures of concrete plays a critical role in the safety of concrete projects.Ultrasonic nondestructive testing has become a widely used technique for identifying defects in concrete structures.However,it is difficult to adequately detect such defects through technical inspections and traditional defect detection methods.Therefore,it proposes a one-dimensional convolutional neural network(1D-CNN)based on a finite element simulation database for various defects in concrete.The raw ultrasonic signal was imported into the model and the accuracy and robustness of 1D-CNN model were validated by introducing Gaussian white noise.The fault detection capabilities of CNN model was then verified through physical testing.Experimental results show that the accuracy of the 1D-CNN model exceeds 83%which outperforms other machine learning methods.Indicating the high efficacy of CNN model in distinguishing the defects of concrete.
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