Abstract Across multiple construction processes, the cup-lock scaffolding systems have been widely applied as temporary facilities, while several catastrophes caused by scaffolding collapse have been reported. Therefore, in this paper, we conduct an exploratory study to attempt to research one issue that can affect the stability of scaffolding systems, namely the looseness of the cup-lock joint. In this paper, to detect looseness of cup-lock scaffold, we develop a new percussion-based method to avoid current structural health monitoring (SHM) methods that depend on constant contact between structures and sensors. Particularly, inspired by the rapid development of automatic speech recognition (ASR), we propose a convolutional bi-directional long short-term memory (CBLSTM) model to classify the Mel frequency cepstral coefficient (MFCC) features extracted from percussion-induced sound signals. To the best of our knowledge, this is the first application of ASR technique in looseness detection of cup-lock scaffold. The working mechanism of CBLSTM is given as follows: a convolutional neural network (CNN) is used to craft characteristics from MFCC features, and a bi-directional long short-term memory architecture (BLSTM) can improve classification accuracy by assimilating the learned CNN features. Finally, a laboratory experiment is conducted to verify the effectiveness of the proposed method, and we demonstrate that CBLSTM outperforms the CNN and BLSTM in classifying the MFCC features. Overall, the percussion-based method proposed in this paper can provide a new direction for the investigation, particularly health monitoring, on the cup-lock scaffolding system.

中文翻译：

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使用基于敲击的方法在杯锁脚手架中进行松动检测

摘要 在多个施工过程中，杯锁式脚手架系统作为临时设施得到了广泛的应用，同时也报道了多起脚手架倒塌造成的灾难。因此，在本文中，我们进行了一项探索性研究，试图研究一个会影响脚手架系统稳定性的问题，即杯锁接头的松动。在本文中，为了检测杯锁脚手架的松动，我们开发了一种新的基于冲击的方法，以避免当前依赖于结构和传感器之间持续接触的结构健康监测 (SHM) 方法。特别是受到自动语音识别（ASR）快速发展的启发，我们提出了一个卷积双向长短期记忆 (CBLSTM) 模型来对从敲击声信号中提取的 Mel 频率倒谱系数 (MFCC) 特征进行分类。据我们所知，这是 ASR 技术在杯锁脚手架松动检测中的首次应用。CBLSTM 的工作机制如下：使用卷积神经网络 (CNN) 从 MFCC 特征中制作特征，双向长短期记忆架构 (BLSTM) 可以通过同化学习的 CNN 特征来提高分类精度. 最后，通过实验室实验来验证所提出方法的有效性，我们证明 CBLSTM 在对 MFCC 特征进行分类方面优于 CNN 和 BLSTM。全面的，