Damage Detection Using Wavelet Packet Decomposition and Random Forests Algorithm in Experimental Structure at the UBC (University of British Columbia)

Document Type : Original Article

Authors

1 Master of Science in Civil Engineering, Tabriz University.

2 Associate Professor, Faculty of Engineering, Tabriz University.

Abstract

Damage Detection methods based on signal are principal and widely used methods that contain wavelet packet decomposition, which is one of new methods in this field. On the other hand there are lots of methods and implements for evaluate models which classify data and regression.Random Forests ,which is newly used method has attracted researchers attention. In this paper a experimental structure was designed and analyzed. drifts from time history response were decomposed to energy rate indexes by wavelet packet decomposition. energy rate indexes in each damage conditions were classified in 3 class of damage conditions and they made data base. finally by training the algorithm, R-F tested other conditions with comparing data base and other near damage conditions, and classified in one of 3 classes. Random Forests precision in this research was 83% which is admissible for classifying. this algorithm can be used on other researches in the future time.

Keywords

References

[1] Shahsavari, H., Baghani, M., Sohrabpour, S., & Naghdabadi, R. (2016). “Continuum damage-healing constitutive modeling for concrete materials through stress spectral decomposition”, International Journal of Damage Mechanics, 25(6), 900-918.
[2] Jiang, X., & Adeli, H. (2005). “Dynamic wavelet neural network for nonlinear identification of highrise buildings”, Computer-Aided Civil and Infrastructure Engineering, 20(5), 316-330.
[3] Breiman, L. (2001). “Random forests”, Machine learning, 45(1), 5-32.
[4] Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). “An assessment of the effectiveness of a random forest classifier for land-cover classification”, ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93-104.
[5] Taha, M. R., Noureldin, A., Lucero, J. L., & Baca, T. J. (2006). “Wavelet transform for structural health monitoring: a compendium of uses and features”, Structural Health Monitoring, 5(3), 267-295.
[6] Wu, N., & Wang, Q. (2011). “Experimental studies on damage detection of beam structures with wavelet transform”, International Journal of Engineering Science, 49(3), 253-261.
[7] Yi, T. H., Li, H. N., & Sun, H. M. (2013). “Multi-stage structural damage diagnosis method based on”, Smart Structures and Systems, 12(3_4), 345-361.
[8] Cruz, P. J., & Salgado, R. (2009). “Performance of vibration-based damage detection methods in bridges”, Computer-Aided Civil and Infrastructure Engineering, 24(1), 62-79.
[9] Sun, Z., & Chang, C. C. (2002). “Structural damage assessment based on wavelet packet transform”, Journal of structural engineering, 128(10), 1354-1361.
[10] Lotfollahi-Yaghin, M. A., & Koohdaragh, M. (2011). “Examining the function of wavelet packet transform (WPT) and continues wavelet transform (CWT) in recognizing the crack specification”, KSCE Journal of Civil Engineering, 15(3), 497-506.
[11] Zhou, Q., Zhou, H., Zhou, Q., Yang, F., & Luo, L. (2014). “Structure damage detection based on random forest recursive feature elimination”, Mechanical Systems and Signal Processing, 46(1), 82-90.
[12] Chandrashekar, G., & Ferat, S. (2014). “A survey on feature selection methods”, Computers & Electrical Engineering, 40(1), 16-28.
[13] Misiti, M., Misiti, Y., Oppenheim, G., & Poggi, J. M. (2004). Matlab Wavelet Toolbox User's Guide. Version 3.
[14] Ren, W. X., Sun, Z. S., Xia, Y., Hao, H., & Deeks, A. J. (2008). “Damage identification of shear connectors with wavelet packet energy: laboratory test study”, Journal of structural engineering, 134(5), 832-841.
[15] Prakash, R., Sivakumar, E., & Srinivasan, M. (2013). “Wavelet packet transform based damage identiļ¬cation of GFRP beam”, Journal of Structural Engineering, 40(1), 44-47.
[16] Biau, G. (2012). “Analysis of a random forests model”, Journal of Machine Learning Research, 13(Apr), 1063-1095.
[17] Biau, G., Devroye, L., & Lugosi, G. (2008). “Consistency of random forests and other averaging classifiers”, Journal of Machine Learning Research, 9(Sep), 2015-2033.
[18] Quinlan, J. R. (1986). “Induction of decision trees”, Machine learning, 1(1), 81-106.
[19] Dyke, S. J., Bernal, D., Beck, J., & Ventura, C. (2003). “Experimental phase II of the structural health monitoring benchmark problem”, In Proceedings of the 16th ASCE engineering mechanics conference.
Send comment about this article
CAPTCHA Image

Files

Share

How to cite

Statistics