A percussion method with attention mechanism and feature aggregation for detecting internal cavities in timber

Abstract

Timber structures have been widely utilized in the field of construction. Nevertheless, these structures are susceptible to environmental factors that can cause internal damage, including cavities, cracks, and corrosion, which can significantly compromise their structural integrity and safety. However, the detection of such internal damages is often challenging due to their concealed nature. Existing methods for detecting internal damage in timber structures suffer from low detection efficiency and recognition accuracy. This study proposes an end-to-end damage detection framework that integrates emphasized channel attention, propagation, and aggregation-time-delay neural network (ECAPA-TDNN) with percussion techniques. This framework is designed to evaluate the internal damage status of timber structures. The paper also introduces a method for creating internal voids within timber structures and fabricates timber specimens containing various sizes of internal damage. Vibration responses at different locations on the specimens are obtained using percussion techniques and serve as input data for the network. The results indicated that the ECAPA-TDNN architecture could effectively identifies damage within the timber structure and accurately distinguish between different sizes of damage, furnishing superlative recognition performance transcending prevailing benchmark models. This study elucidates the potential of percussion-based techniques and deep learning methods in the damage detection field, as well as the possibility for the proposed method to be extended to other applications, including transformer inspection, vehicle vibration monitoring, and medical treatment.