Timber structures have been a dominant form of construction throughout most of history and continued to serve as a widely used staple of civil infrastructure in the modern era. As a natural material, wood is prone to termite damages, which often cause internal cavities for timber structures. Since internal cavities are invisible and greatly weaken structural load-bearing capacity, an effective method to timber internal cavity detection is of great importance to ensure structural safety. This article proposes an innovative deep neural network (DNN)–based approach for internal cavity detection of timber columns using percussion sound. The influence mechanism of percussion sound with the volume change of internal cavity was studied through theoretical and numerical analysis. A series of percussion tests on timber column specimens with different cavity volumes and environmental variations were conducted to validate the feasibility of the proposed DNN-based approach. Experimental results show high accuracy and generality for cavity severity identification regardless of percussion location, column section shape, and environmental effects, implying great potentials of the proposed approach as a fast tool for determining internal cavity of timber structures in field applications.

在历史的大部分时间里，木材结构一直是主要的建筑形式，并在现代继续作为广泛使用的民用基础设施。作为一种天然材料，木材容易受到白蚁危害，这往往会导致木结构内部出现空洞。由于内部空洞是不可见的，大大削弱了结构的承载能力，因此有效的木材内部空洞检测方法对于确保结构安全具有重要意义。本文提出了一种基于深度神经网络 (DNN) 的创新方法，用于使用敲击声检测木柱的内部空腔。通过理论和数值分析，研究了敲击声随内腔体积变化的影响机理。对具有不同腔体积和环境变化的木柱标本进行了一系列冲击测试，以验证所提出的基于 DNN 的方法的可行性。实验结果表明，无论冲击位置、柱截面形状和环境影响如何，空腔严重程度识别的准确性和通用性都很高，这意味着所提出的方法作为在现场应用中确定木结构内部空腔的快速工具具有巨大潜力。