A New Image-Based Soil Deformation Measurement System

Abstract:

A Soil Deformation Measurement System using OPENCV library and FFTW library in C++ was developed in this paper. The system applied camera calibration based on neural network and Fasst Fourier Transform (FFT) cross-correlation algorithm for Particle Image Velocimetry (PIV). It is used to obtain soil deformation data, such as displacements, velocity and strain, and visualize the deformation. Experiments show that this system could acquire deformation data from soil images accurately, efficiently and continuously, which provides a strong proof that image processing technology has practical significance and application value in the research field of geotechnical engineering.

• Periodical: Advanced Materials Research (Volume 629)
• Pages: 488-492
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.629.488
• Online since: December 2012
• Authors: Yan Jiao Zhao, Yu Xin Wang, Guo He, Hong Hua Zhao
• Keywords: FFT-Based Cross-Correlation, Neural Network (NN), Soil Deformation Measurement, Visualization

References

[1] Donohoe GW, Bacobella MF, Gill JJ. Segmentation and Object Tracking for the Microstructure Analysis of Soil. IEEE Conference on Signals, Systems and Computers, Pacific Grove CA 1991; 2: 1215–1219.

DOI: 10.1109/acssc.1991.186641

[2] Zhang G, Liang DF, Zhang JM. Image analysis measurement of soil particle movement during a soil–structure interface test. J Computers and Geotechnics 2006. 33( 4-5) : 248-259.

DOI: 10.1016/j.compgeo.2006.05.003

[3] White D, Randolph M, Thompson B. An Image-Based DeformationMeasurement System for the Geotechnical Centrifuge. J International Journal of PhysicalModelling in Geotechnics 2005; 5(3): 1–12.

[4] White D J, Take W A, Bolton M D. Soil Deformation Measurement Using Particle Image Velocimetry (PIV) and Photogrammetry. J Geotechnique 2003; 53(7): 619–631.

DOI: 10.1680/geot.2003.53.7.619

[5] Wieneke B. Volume self-calibration for 3D particle image velocimetry. J Experiments in Fluids 2008; 45(4): 549-556.

DOI: 10.1007/s00348-008-0521-5

[6] Daiyan H, Andreassen E, Grytten F, Osnes H, Gaarder RH. Shear Testing of PolypropyleneMaterials Analysed by Digital Image Correlation and Numerical Simulations. J Experimental Mechanics (2012).

DOI: 10.1007/s11340-012-9591-7

[7] Shah SG and Chandra Kishen JM. Fracture Properties of Concrete–Concrete Interfaces Using Digital Image Correlation. J Experimental Mechanics 2011; 51(3): 303-313.

DOI: 10.1007/s11340-010-9358-y

[8] Heikkila J, Silven O. A Four-Step Camera Calibration Procedure with Implicit Image Correction. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Juan, Puerto Rico; 1997, p.1106–1112.

DOI: 10.1109/cvpr.1997.609468

[9] Bouguet, J. -Y., 2006, Camera Calibration Toolbox forMatlab, Computer Vision Research Group, Department of Electrical Engineering, California Institute of Technology, http: /www. vision. caltech. edu/bouguetj/calib_doc/index. html.

[10] Honghua Zhao, Louis Ge. Camera Calibration Using Neural Network for Image-Based Soil Deformation Measurement Systems. J Geotechnical Testing Journal 2008; 31(2): 192-197.

DOI: 10.1520/gtj100729

[11] Sveen J K. An Introduction to MatPIV 2004, v. 1. 6. 1. Eprint No. 2, ISSN 0809-4403, Dept. of Math., University of Oslo.

[12] Bao Xiaoli, Li. Muguo Improvement of FFT-based cross-correlation algorithm for PIV. J Dalian Ligong Daxue Xuebao 2011; 51(3): 417-421. Language: Chinese.