Study on Damage Identification of Beam Bridge Based on Characteristic Curvature and Improved Wavelet Threshold De-Noising Algorithm

Study on Damage Identification of Beam Bridge Based on Characteristic Curvature and Improved Wavelet Threshold De-Noising Algorithm

Xi Chu* Zhixiang Zhou Guojun Deng Tengjiao Jiang Yangkun Lei

Dept. of State Key Laboratory Breeding Base of Mountain Bridge Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, China

Corresponding Author Email:
12 June 2017
18 June 2017
30 June 2017
| Citation



With the point cloud data of box girder obtained by the theory of structure from motion (SFM) algorithm chosen as the research background, a damage identification method based on characteristic curvature and improved wavelet threshold de-noising algorithm is presented. Firstly, the static load test is carried out for the full-scale box girder model, and after the cracking damage, the discrete point cloud data on the surface of the box girder are obtained through the SFM theory. According to the basic hypothesis, deformation is mainly caused by the bending moment, and micro damage has no effect on stress redistribution. Therefore, a conclusion can be made that the curvature is sensitive to the structural damage. Then, a characteristic curvature damage identification method, which is based on point cloud chord length, is used to solve the characteristic curvature of the specified section of the box girder. It is found that there are a large number of point cloud noise signals in the characteristic curvature, and on this basis, a new wavelet de-noising method established upon the threshold function is proposed. Finally, the damage index revealed from the characteristic curvature after de-noising in the specified section is compared with the actual damage location of the box girder. The results show that by combining the characteristic curvature algorithm based on the chord length of scattered point cloud with the improved wavelet threshold de-noising function, the structural surface crack damage based on spatial point cloud data can be identified. Especially, in consideration of noise interference, the improved wavelet threshold de-noising function proposed in this paper can effectively suppress the high frequency noise signal in the characteristic curvature and preserve the low-frequency signal of damage in the damage area. Thus, the accuracy and sensitivity of the damage identification method based on the characteristic curvature are guaranteed. This method has the potential to be applied to structural health monitoring, since it can provide a new technical method for the early warning of the beam structure bridge.


Damage identification, Feature curvature, SFM, Threshold function, Wavelet de-noising

1. Introduction
2. The Establishment of Digital Three-Dimensional Model for the Box Girder Damage Model
3. The Damage Identification Method Based on Characteristic Curvature
4. Study on Wavelet De-Noising Algorithm Based on Improved Threshold
5. Conclusions

[1] D.G. Lowe, Distinctive image features from scale-invariant keypoints, 2004, International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110.

[2] I. Gordon, D.G. Lowe, What and where: 3D object recognition with accurate pose, 2006, Toward Category-Level Object Recognition, vol. 4170, pp. 67-82.

[3] Y. Furukawa, J. Ponce, Carved visual hulls for image-based modeling, 2009, International Journal of Computer Vision, vol. 81, no. 1, pp.53-67.

[4] Y. Furukawa, High-fidelity image-based modeling, 2008, Dissertations & Theses - Gradworks, vol. 12, no. 2, pp. 1825-1860.

[5] N. Snavely, S.M. Seitz, R. Szeliski, Modeling the world from internet photo collections, 2008, International Journal of Computer Vision, vol. 80 no. 2, pp. 189-210.

[6] M.J. Westoby, J. Brasington, N.F. Glasser, M.J. Hambrey, J.M. Reynolds, ‘structure-from-motion’ photogrammetry: A low-cost, effective tool for geoscience applications, 2012, Geomorphology, vol. 179, pp. 300-314.

[7] S. Harwin, A. Lucieer, Assessing the accuracy of georeferenced point clouds produced via multi-view stereopsis from unmanned aerial vehicle (UAV) imagery, 2012, Remote Sensing, vol. 4, no. 6, pp. 1573-1599.

[8] T.N. Tonkin, N.G. Midgley, D.J. Graham, J.C. Labadz, The potential of small unmanned aircraft systems and structure-from-motion for topographic surveys: a test of emerging integrated approaches at cwm idwal, north wales, 2014, Geomorphology, vol. 226, no. 1-2, pp. 35–43.

[9] A. Lucieer, D. Turner, D.H. King, S.A. Robinson, Using an unmanned aerial vehicle (UAV) to capture micro-topography of antarctic moss beds, 2013, International Journal of Applied Earth Observation & Geoinformation, vol. 27, no. 4, pp. 53-62.

[10] C. Wu, S. Agarwal, B. Curless, S.M. Seitz, Multicore bundle adjustment, 2011, Computer Vision and Pattern Recognition, vol. 42, pp. 3057-3064.

[11] K. Wolff, C. Kim, H. Zimmer, C. Schroers, M. Botsch, O. Sorkine-Hornung, Point cloud noise and outlier removal for image-based 3D reconstruction, 2016, Fourth International Conference on 3d Vision, pp. 118-127.

[12] D.L. Donoho, De-noising by soft-thresholding, 1995, IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613-627.

[13] S. Sardy, P. Tseng, A. Bruce, Robust wavelet denoising, 2001, IEEE Transactions on Signal Processing, vol. 49, no. 6, pp. 1146-1152.

[14] C. Huimin, Z. Ruimei, H. Yanli, Improved threshold denoising method based on wavelet transform, 2012, Physics Procedia, vol. 33, no. 1, pp. 1354-1359.

[15] K.N. Snavely, Scene reconstruction and visualization from internet photo collections, 2008, Transactions on Computer Vision & Applications, vol. 3, no. 12, pp. 1909-1911.

[16] N. Snavely S.M. Seitz, R. Szeliski, Skeletal graphs for efficient structure from motion, 2008, Computer Vision and Pattern Recognition, vol. 1, pp. 1-8.

[17] R. Hartley, Zisserman, A., Multiple view geometry in computer vision, 2001, Kybernetes, vol. 30, no. 9/10, pp. 1865 - 1872. 

[18] L.M. Shi, F.S. Guo, Z.Y. Hu, An improved PMVS through scene geometric information, 2011, Journal of Automatica Sinica, vol. 37, no. 5, pp. 560-568.

[19] E. Güzel, M. Canyilmaz, M. Türk, Application of wavelet-based denoising techniques to remote sensing very low frequency signals, 2016, Radio Science, vol. 46, no. 2, pp. 1-9. 

[20] X. Dong, S. Mane, Z. Sosic, Characterization of signals, 2015, Electrophoresis, vol. 36, no. 2, pp. 363–370.

[21] H. Douzi, D. Mammass, F. Nouboud, Faber-schauder wavelet transform, application to edge detection and image characterization, 2001, Journal of Mathematical Imaging and Vision, vol. 14, no. 2, pp. 91-101.