Automatic Water Seepage Depth Detection in Concrete Structures Using Percussion Method Combined With Deep Learning Network

Abstract

Water seepage in concrete can significantly degrade the durability of hydraulic concrete structures. Therefore, this paper introduces a new method that combines the percussion method with deep learning techniques to detect the depth of water seepage in concrete structures. Initially, percussion sound signals were collected for different water seepage depths. Then, the proposed one-dimensional convolutional bidirectional gated recurrent unit (BiGRU) network with wide first-layer kernel (1D-WCBGRU) classifies the percussion sound signals for different water seepage depths. The 1D-WCBGRU uses a wide first convolutional kernel to extract features directly from the original percussion signals without the need to extract features manually. Subsequently, the BiGRU is utilized to capture long short-term information from the data, thereby enhancing feature separability and improving the classification accuracy and robustness of the model. Experiments confirm that the 1D-WCBGRU exhibits excellent performance in the seepage depth detection task compared to traditional learning algorithms.