LAUSR

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.