Defect Identification of Concrete Piles Based on Numerical Simulation and Convolutional Neural Network

Abstract

Defects in pile foundations, such as neck defects, bulge imperfections, weak concretes, cracks, and broken piles, can cause a decrease in the bearing capacity and the structural stability of the foundation. Identification of the type of defect is vital in formulating a reasonable repair plan for the pile foundation. In this study, the authors proposed a scheme to identify the types of defects in concrete piles based on a convolution neural network and a low-strain pile integrity test (LSPIT). A batch modeling method of defective pile foundations using Python script was also proffered. The different degrees of signals of five types of defective pile foundations were simulated by this method. The original data were decomposed and reconstructed by wavelet packet decomposition (WPT). To prevent the data from losing too much information after WPT, the data of 400 × 1 after decomposition and reconstruction were processed by dimension-raising to obtain the data of 20 × 20 × 1. Then, the multidimensional feature index of 20 × 20 × 2 was generated by index fusion with the original data. Finally, the data were input onto convolutional neural network (CNN) as a training parameter. Following an improvement of the dataset, the recognition accuracy of the type of defect in the pile foundation by the proposed identification scheme reached 94.4%.