Experimental study on joint stiffness with vision-based system and geometric imperfections of temporary member structure



Published Mar 9, 2018
Cong LIU Lin HE Zhenyu WU Jian YUAN


In this paper, tests of plug-pin joints are conducted in order to obtain their mechanical parameters, including semi-rigid property. To solve the difficulties of multi-point displacement measurements for small joints, this investigation proposes a vision-based measurement system based on the principle of binocular stereo vision to improve measurement accuracy. Accurate sub-pixel location is achieved according to a template-matching algorithm based on grayscale. Joint performance, including horizontal bar joint tension and compression, semi-rigidity between horizontal bars and upright rods and bracing tension and compression, is investigated in order to acquire joint failure modes as well as load and displacement (or moment and rotation angle) curves. Through data fitting, multi-linear simplified models are proposed to illustrate the joints’ mechanical performance. This paper also investigates geometric imperfection of temporary member structure with plug-pin joints based on several substructure models and temporary grandstand units using a total station theodolite. The probabilistic models of initial member out-of-straightness and story frame out-of-plumb have been acquired, which can be used into Monte Carlo simulation to create stochastic model of the temporary member structure.

Copyright © 2018 The Author(s). Published by VGTU Press This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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temporary member structure, plug-pin joint, vision-based measurement system, multi-linear simplified model, initial geometric imperfection, probabilistic model

Armesto, J.; Lubowiecka, I.; Ordóñez, C.; Rial, F. I. 2009. FEM modeling of structures based on close range digital photogrammetry, Automation in Construction 18: 559–569. https://doi.org/10.1016/j.autcon.2008.11.006

Beale, R. G. 2014. Scaffold research – a review, Journal of Constructional Steel Research 98: 188–200. https://doi.org/10.1016/j.jcsr.2014.01.016

Bell, E. S.; Peddle, J. T.; Goudreau, A. 2012. Bridge condition assessment using digital image correlation and structural modeling, in Proceedings of the 6th International Conference on Bridge Maintenance, Safety, Management, Resilience and Sustainability, 8–12 July 2012, Maggiore, Italy. https://doi.org/10.1201/b12352-41

Bielewicz, E.; Goórski, J. 2002. Reliability of imperfect structures (simple non-linear models), Journal of Civil Engineering and Management 8(2): 83–87. https://doi.org/10.1080/13923730.2002.10531256

Chan, S.; Huang, H.; Fang, L. 2005. Advanced analysis of imperfect portal frames with semirigid base connections, Journal of Engineering Mechanics 131: 633–640. https://doi.org/10.1061/(ASCE)0733-9399(2005)131:6(633)

Chandrangsu, T.; Rasmussen, K. J. 2011a. Investigation of geometric imperfections and joint stiffness of support scaffold systems, Journal of Constructional Steel Research 67: 576–584. https://doi.org/10.1016/j.jcsr.2010.12.004

Chandrangsu, T.; Rasmussen, K. J. R. 2011b. Structural modelling of support scaffold systems, Journal of Constructional Steel Research 67: 866–875. https://doi.org/10.1016/j.jcsr.2010.12.007

Chang, C.; Ji, Y. 2007. Flexible videogrammetric technique for three-dimensional structural vibration measurement, Journal of Engineering Mechanics 133: 656–664. https://doi.org/10.1061/(ASCE)0733-9399(2007)133:6(656)

Di, S.; Lin, H.; Du, R. 2011. Two-dimensional (2D) displacement measurement of moving objects using a new MEMS binocular vision system, Journal of Modern Optics 58: 694–699. https://doi.org/10.1080/09500340.2011.566636

Fu, G.; Moosa, A. G. 2002. An optical approach to structural displacement measurement and its application, Journal of Engineering Mechanics 128: 511–520. https://doi.org/10.1061/(ASCE)0733-9399(2002)128:5(511)

Fukuda, Y.; Feng, M. Q.; Shinozuka, M. 2010. Cost‐effective vision‐based system for monitoring dynamic response of civil engineering structures, Structural Control and Health Monitoring 17: 918–936. https://doi.org/10.1002/stc.360

GB 50017-2003. Code for design of steel structure. Chinese standard, 2003.

Ghosh, P. K.; Mudur, S. P. 1995. Three-dimensional computer vision: A geometric viewpoint, Computer Journal 38: 85–86. https://doi.org/10.1093/comjnl/38.1.85

Godley, M. H. R.; Beale, R. G. 1997. Sway stiffness of scaffold structures, Structural Engineer 75(1): 4–12.

Jáuregui, D. V.; White, K. R.; Woodward, C. B.; Leitch, K. R. 2003. Noncontact photogrammetric measurement of vertical bridge deflection, Journal of Bridge Engineering 8: 212–222. https://doi.org/10.1061/(ASCE)1084-0702(2003)8:4(212)

JGJ 162-2008. Technical code for safety of forms in construction. Chinese standard, 2008.

Jiang, R.; Jáuregui, D. V.; White, K. R. 2008. Close-range photogrammetry applications in bridge measurement: literature review, Measurement 41: 823–834. https://doi.org/10.1016/j.measurement.2007.12.005

Kala, Z. 2012. Geometrically non-linear finite element reliabil¬ity analysis of steel plane frames with initial imperfections, Journal of Civil Engineering and Management 18(1): 81–90. https://doi.org/10.3846/13923730.2012.655306

Kala, Z.; Valeš, J.; Jönsson, J. 2017. Random fields of initial out of straightness leading to column buckling, Journal of Civil Engineering and Management 23(7): 902–913. https://doi.org/10.3846/13923730.2017.1341957

Lee, J. J.; Shinozuka, M. 2006. A vision-based system for remote sensing of bridge displacement, Ndt & E International 39: 425–431. https://doi.org/10.1016/j.ndteint.2005.12.003v

Liu, H.; Jia, L.; Wen, S.; Liu, Q.; Wang, G.; Chen, Z. 2016. Experimental and theoretical studies on the stability of steel tube–coupler scaffolds with different connection joints, Engineering Structures 106: 80–95. https://doi.org/10.1016/j.engstruct.2015.10.015

Lord, J.; Mccormick, N. 2012. Digital image correlation for structural measurements, in Proceedings of the Institution of Civil Engineers - Civil Engineering, November 2012, Vol. 165(4): 185–190. https://doi.org/10.1680/cien.11.00040

Morlier, J.; Salom, P.; Bos, F. 2007. New image processing tools for structural dynamic monitoring, Key Engineering Materials 347: 239–244. https://doi.org/10.4028/www.scientific.net/KEM.347.239

Peng, J. L.; Wu, C. W.; Chan, S. L.; Huang, C. H. 2013. Experimental and numerical studies of practical system scaffolds, Journal of Constructional Steel Research 91: 64–75. https://doi.org/10.1016/j.jcsr.2013.07.028

Tsai, R. Y. 1987. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses, IEEE Journal on Robotics & Automation 3: 323–344. https://doi.org/10.1109/JRA.1987.1087109

Yun, C. B.; Lee, J. J.; Koo, K. Y. 2011. Smart structure technologies for civil infrastructures in Korea: recent research and applications, Structure & Infrastructure Engineering 7: 673–688. https://doi.org/10.1080/15732470902720109

Zhang, Z. 2000. A flexible new technique for camera calibration, IEEE Transactions on Pattern Analysis & Machine Intelligence 22: 1330–1334. https://doi.org/10.1109/34.888718