Detection Method for Bolted Connection Looseness at Small Angles of Timber Structures based on Deep Learning

Bolted connections are widely used in timber structures. Bolt looseness is one of the most important factors leading to structural failure. At present, most of the detection methods for bolt looseness do not achieve a good balance between cost and accuracy. In this paper, the detection method of small angle of bolt loosening in a timber structure is studied using deep learning and machine vision technology. Firstly, three schemes are designed, and the recognition targets are the nut's own specification number, rectangular mark, and circular mark, respectively. The Single Shot MultiBox Detector (SSD) algorithm is adopted to train the image datasets. The scheme with the smallest identification angle error is the one identifying round objects, of which the identification angle error is 0.38 degrees. Then, the identification accuracy was further improved, and the minimum recognition angle reached 1 degrees. Finally, the looseness in a four-bolted connection and an eight-bolted connection are tested, confirming the feasibility of this method when applied on multi-bolted connection, and realizing a low operating costing and high accuracy.

Automated summary

This study investigates the method of detecting bolt looseness in timber structures using deep learning and machine vision technology. Through training models and testing, it achieves the goal of low cost and high accuracy in multi-bolted connections.