An innovative deep neural network coordinating with percussion-based technique for automatic detection of concrete cavity defects

Abstract

Cavity defects are usually invisible in concrete, failure to detect and repair them in time could significantly weaken the load-bearing capacity of engineering structures, potentially threatening the structural safety during the construction and operation periods. Therefore, it is essential to explore efficient approaches to detect concrete cavity defects. In this study, we propose a novel method to detect concrete cavity defects based on percussion and deep learning (DL) techniques. First, the percussion sound dataset was collected from concrete specimens with different types of precast cavity defects. Next, an innovative DL framework named the multi-scale convolutional bidirectional memory network (MCBMNet) was developed for classifying these percussion sound signals associated with specified concrete cavity defects. The MCBMNet utilizes the multi-scale convolutional operation to generate various scale features directly from raw percussion sounds, and then employs the bidirectional memory units to capture intrinsic feature connections and enhance feature separability, thus improving prediction accuracy and robustness. Finally, two experimental cases, including water-free and water-filled conditions, were designed to test the feasibility of the proposed method considering the influences of humidity environment in reality. Experimental results well demonstrate the excellent performances of the proposed MCBMNet for concrete cavity defect detection, with its average accuracy over 98% in both cases. Additionally, the anti-noise capacity and adaptability of MCBMNet were thoroughly investigated, which outperforms conventional learning algorithms.