PSI - Issue 37

Francisco Barros et al. / Procedia Structural Integrity 37 (2022) 880–887 Barros et al./ Structural Integrity Procedia 00 (2019) 000 – 000

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5. Conclusions A monitoring setup based on 2D and 3D digital image correlation was successfully implemented for the measurement of displacement on a pedestrian bridge, using a nearby building as a support to install the vision systems. The assembled system was able to measure displacements on the bridge on the order of magnitude of a few millimetres, as it was designed to do. The 3D and 2D results agreed perfectly with each other and with the expected behaviour. The use of natural features of the bridge as a speckle pattern was successful, even in the 2D setup where they had lower contrast and were less consistent. It was shown that a DIC test on an urban structure is possible, with good performance, without preparing the surface with a painted pattern beforehand. In general terms, it was demonstrated that it is possible to implement a displacement monitoring system based on computer vision which is physically supported on existing urban structures, requiring little intervention in the structure being monitored. Acknowledgements The authors acknowledge the financial support received from P2020 project number POCI-01-0247-FEDER 041435, Safe Cities - Inovação para Construir Cidades Seguras, cofinanced by European Union (EU) through the FEDER (European Regional Development Fund) under of the COMPETE 2020 (Operational Program for Competitiveness and Internationalization). References Acikgoz, S., DeJong, M. J. & Soga, K., 2018. Sensing dynamic displacements in masonry rail bridges using 2D digital image correlation. Structural Control and Health Monitoring, 25(8). Barros, F., Sousa, P. J., Tavares, P. J. & Moreira, P. M. G. P., 2018. Digital image correlation through image registration in the frequency domain. The Journal of Strain Analysis for Engineering Design, 53(8), p. 575 – 583. Guizar-Sicairos, M., Thurman, S. T. & Fienup, J. R., 2008. Efficient subpixel image registration algorithms. Optics Letters, 33(2), pp. 156-158. Hartley, R. & Zisserman, A., 2003. Multiple View Geometry in Computer Vision. s.l.:Cambridge University Press. Lowe, D. G., 2004. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, 60(2), pp. 91-110. The MathWorks, Inc., 2021. Estimate geometric transform from matching point pairs - MATLAB estimateGeometricTransform. [Online] Available at: https://www.mathworks.com/help/vision/ref/estimategeometrictransform.html Tung, S.-H., Weng, M.-C. & Shih, M.-H., 2013. Measuring the in situ deformation of retaining walls by the digital image correlation method. Engineering Geology, Volume 166, pp. 116-126. Vedaldi, A. & Fulkerson, B., 2008. VLFeat: An Open and Portable Library. [Online] Available at: http://www.vlfeat.org/ Verstrynge, E. et al., 2018. Crack monitoring in historical masonry with distributed strain and acoustic emission sensing techniques. Construction and Building Materials, Volume 162, pp. 898-907. Winkler, J. & Hendy, C., 2017. Improved Structural Health Monitoring of London’s Docklands Light Railway Bridges Using Digita l Image Correlation. Structural Engineering International, 27(3), pp. 435-440. Zhang, Z., 2000. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), pp. 1330-1334.