Automatic moisture content detection in concrete based on percussion method combined with deep learning
Wenjie Huang, Longguang Peng, Zezhong Zheng, Jicheng Zhang, Xingxing Chen, Bowen Zhou, Kai Zhou, Zhiyun Zhang
Abstract
Water in concrete significantly affects its durability, so detection of water content in concrete is essential to ensure its durability and safety. This paper introduces a method for detecting moisture content in concrete structures utilizing percussion and deep learning techniques. The method deploys a deep neural network that automatically classifies moisture content. A two-stream convolutional bi-directional long short-term memory network (TS-CBLSTM) directly processes the acquired percussion acoustic signals with different moisture content. The TS-CBLSTM employs a two-stream convolutional operation to extract features inherent in the two channels of the original audio. Subsequently, a bi-directional long short-term memory (BiLSTM) block captures the connectivity of intrinsic features, thereby enhancing feature separability. This approach improves the classification accuracy and robustness. The experimental results show that TS-CBLSTM performs brilliantly in concrete moisture content detection with 100% classification accuracy. Furthermore, the intensive study of TS-CBLSTM’s noise immunity and adaptability confirms that it outperforms conventional algorithms.