Image-based Bolt-loosening Detection Technique of Bolt Joint in Steel Bridges

This paper presents a novel bolt-loosening detection technique using image information of bolted joints in steel bridges. Firstly, existing bolt-loosening detection techniques are reviewed and their benefits and limitations are analyzed. Secondly, a bolt-loosening detection algorithm using image processing techniques is newly proposed for bolted joints in steel bridges. It consists of 3 steps: (1) taking a picture for a bolt joint, (2) segmenting the image to identify a splice plate and each nut, and (3) identifying rotation angle of each nut and detecting boltloosening. As a key technique, the Hough transform is used to identify rotation angles of nuts, and then boltloosening is detected by comparing the angles before and after bolt-loosening. Finally, the applicability of the proposed technique is evaluated by experimental tests with bolt-loosening scenarios. A bolted joint model which consists of a splice plate and 8 sets of bolts and nuts with 2×4 array is used for the tests.

[1]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Maria Q. Feng,et al.  Vision-Based Displacement Sensor for Monitoring Dynamic Response Using Robust Object Search Algorithm , 2013 .

[4]  Young-Soo Park,et al.  An efficient image-based damage detection for cable surface in cable-stayed bridges , 2013 .

[5]  Jeong-Tae Kim,et al.  Temperature-Compensated Damage Monitoring by Using Wireless Acceleration-Impedance Sensor Nodes in Steel Girder Connection , 2012, Int. J. Distributed Sens. Networks.

[6]  Jeong-Tae Kim,et al.  Hybrid acceleration-impedance sensor nodes on Imote2-platform for damage monitoring in steel girder connections , 2011 .

[7]  Ikhlas Abdel-Qader,et al.  ANALYSIS OF EDGE-DETECTION TECHNIQUES FOR CRACK IDENTIFICATION IN BRIDGES , 2003 .

[8]  Young-Soo Park,et al.  A Synchronized Multipoint Vision-Based System for Displacement Measurement of Civil Infrastructures , 2012, TheScientificWorldJournal.

[9]  Peggy Subirats,et al.  Automation of Pavement Surface Crack Detection using the Continuous Wavelet Transform , 2006, 2006 International Conference on Image Processing.

[10]  Gangbing Song,et al.  Review of Bolted Connection Monitoring , 2013, Int. J. Distributed Sens. Networks.

[11]  Shuji Hashimoto,et al.  Fast crack detection method for large-size concrete surface images using percolation-based image processing , 2010, Machine Vision and Applications.

[12]  Nohyu Kim,et al.  Measurement of axial stress using mode-converted ultrasound , 2009 .