Ultrasonic Signal Transmission Performance in Bolted Connections of Wood Structures under Different Preloads

Abstract

In industrial applications, bolt connections are simple and economical, contributing to their popularity for use in wood packing boxes. However, they can easily fail when subjected to a continuous vibrational load under usual working conditions such as transportation and hoisting. Based on an ultrasonic technique, nondestructive evaluation can be used to quickly detect large-scale structures, but the complex propagation properties in wood limit its application. To solve this problem, a time-reversal method was adopted to predict the residual preload on bolted connections by focusing on the signals collected by wood structures, which helps to assess the structures’ reliability. In this study, the residual preload of bolted connections in wood structures was predicted using the deep-learning method, LSTM, one-dimensional Resnet and Densenet, and tree classification models. It was confirmed that the use of the time-reversal method for ultrasonic detection focused on the signals transmitted in bolted connections of wood structures and deep-learning methods are a feasible way to predict an ultrasonic transmission model.

Vision-Based Detection of Bolt Loosening Using YOLOv5

“…Compared with traditional methods, methods based on deep learning can autonomously learn the characteristics of data [ 32 , 33 , 34 , 35 , 36 ]. In the field of bolt loosening detection, Zhuang et al [ 37 ] combined the time reversal method with deep learning methods to classify the ultrasonic signals in the bolted connections of wood structures, thus realizing the prediction of residual preload on bolted connections. Cha and Choi et al [ 38 , 39 , 40 ] combined machine vision with support vector machine (SVM) to automatically distinguish tight bolts and loose bolts by detecting horizontal and vertical lengths of bolt heads in images.…”