Vibration-based damage detection of buildings using a decision-tree-based algorithm

Document Type : Research

Authors

1 Kharazmi University

2 PhD Student, Department of Civil Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

Abstract

Previous studies revealed that traditional methods of damage detection (e.g., visual inspection) are time-consuming and require large monetary resources. In the last three decades, machine learning algorithms, sensor technologies, and computer science have progressively advanced, which paved the way for implementing machine learning-based damage detection frameworks. This paper presents a damage detection framework for civil structures using machine learning algorithms. The decision-tree classifier is used to classify the state of damage in the building based on the damage indicators obtained from the output acceleration signals of the building. The braced-frame structure known as the IASC-ASCE structural health monitoring benchmark building was used to verify the presented approach. The total number of 6000 Gaussian white noise signals with 10s length was applied to the case study model as ambient vibrations using the Matlab platform. Five different damage indicators, including the vibration intensity, mean period, mean, variance, and the fundamental frequency of the structure, were used to train the classifier. A Bayesian Optimization algorithm was implemented to tune the hyperparameters of the decision-tree classification learner. The results indicate that the proposed approach could estimate the state of damage in the building with promising accuracy.

Keywords

Main Subjects

References

[1] Bruneau, M., et al., A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake spectra, 2003. 19(4): p. 733-752.
[2] Vahid, R., F. Farnood Ahmadi, and N. Mohammadi, Earthquake damage modeling using cellular automata and fuzzy rule-based models. Arabian Journal of Geosciences, 2021. 14: p. 1-14.
[3] Sajedi, S.O. and X. Liang, A data‐driven framework for near real‐time and robust damage diagnosis of building structures. Structural Control and Health Monitoring, 2020. 27(3): p. e2488.
[4] Azimi, M. and G. Pekcan, Structural health monitoring using extremely compressed data through deep learning. Computer‐Aided Civil and Infrastructure Engineering, 2020. 35(6): p. 597-614.
[5] Cha, Y.J., W. Choi, and O. Büyüköztürk, Deep learning‐based crack damage detection using convolutional neural networks. Computer‐Aided Civil and Infrastructure Engineering, 2017. 32(5): p. 361-378.
[6] Rehman, S.K.U., et al., Nondestructive test methods for concrete bridges: A review. Construction and building materials, 2016. 107: p. 58-86.
[7] Salkhordeh, M., M. Mirtaheri, and S. Soroushian, A decision‐tree‐based algorithm for identifying the extent of structural damage in braced‐frame buildings. Structural Control and Health Monitoring, 2021. 28(11): p. e2825.
[8] Bao, Y., et al., The state of the art of data science and engineering in structural health monitoring. Engineering, 2019. 5(2): p. 234-242.
[9] Khoa, N.L.D., et al., Structural health monitoring using machine learning techniques and domain knowledge based features, in Human and machine learning. 2018, Springer. p. 409-435.
[10] Sen, D. and S. Nagarajaiah, Data-driven approach to structural health monitoring using statistical learning algorithms, in Mechatronics for cultural heritage and civil engineering. 2018, Springer. p. 295-305.
[11] Farrar, C.R. and K. Worden, Structural health monitoring: a machine learning perspective. 2012: John Wiley & Sons.
[12] Ying, Y., et al., Toward data-driven structural health monitoring: application of machine learning and signal processing to damage detection. Journal of Computing in Civil Engineering, 2013. 27(6): p. 667-680.
[13] Wu, X., J. Ghaboussi, and J. Garrett Jr, Use of neural networks in detection of structural damage. Computers & structures, 1992. 42(4): p. 649-659.
[14] Masri, S., et al., Neural network approach to detection of changes in structural parameters. Journal of engineering mechanics, 1996. 122(4): p. 350-360.
[15] Worden, K., G. Manson, and N.R. Fieller, Damage detection using outlier analysis. Journal of Sound and vibration, 2000. 229(3): p. 647-667.
[16] Shih, F.Y. and S. Cheng, Improved feature reduction in input and feature spaces. Pattern Recognition, 2005. 38(5): p. 651-659.
[17] Serpico, S.B., et al. Comparison of feature reduction techniques for classification of hyperspectral remote-sensing data. in Image and Signal Processing for Remote Sensing VIII. 2003. SPIE.
[18] Hyvärinen, A. and E. Oja, Independent component analysis: algorithms and applications. Neural networks, 2000. 13(4-5): p. 411-430.
[19] Abdi, H. and L.J. Williams, Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2010. 2(4): p. 433-459.
[20] De Ridder, D. and R.P. Duin, Locally linear embedding for classification. Pattern Recognition Group, Dept. of Imaging Science & Technology, Delft University of Technology, Delft, The Netherlands, Tech. Rep. PH-2002-01, 2002: p. 1-12.
[21] Reed, J.W. and R.P. Kassawara, A criterion for determining exceedance of the operating basis earthquake. Nuclear Engineering and Design, 1990. 123(2-3): p. 387-396.
[22] Ni, Y., X. Zhou, and J. Ko, Experimental investigation of seismic damage identification using PCA-compressed frequency response functions and neural networks. Journal of sound and vibration, 2006. 290(1-2): p. 242-263.
[23] Neves, A.C., et al., An approach to decision‐making analysis for implementation of structural health monitoring in bridges. Structural Control and Health Monitoring, 2019. 26(6): p. e2352.
[24] Alavi, A.H., et al., Fatigue cracking detection in steel bridge girders through a self-powered sensing concept. Journal of Constructional Steel Research, 2017. 128: p. 19-38.
[25] K Aono, S. Self-powered sensors to facilitate infrastructural internet-of-things for smart structures. in The 13th International Workshop on Advanced Smart Materials and Smart Structures Technology. 2017.
[26] Hasni, H., et al., Self-powered piezo-floating-gate sensors for health monitoring of steel plates. Engineering Structures, 2017. 148: p. 584-601.
[27] Banaei, A., J. Alamatian, and R.Z. Tohidi. Active control of structures using genetic algorithm with dynamic weighting factors using in the constrained objective function. in Structures. 2023. Elsevier.
[28] Johnson, E.A., et al., Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data. Journal of engineering mechanics, 2004. 130(1): p. 3-15.
[29] Caicedo, J.M., S.J. Dyke, and E.A. Johnson, Natural excitation technique and eigensystem realization algorithm for phase I of the IASC-ASCE benchmark problem: Simulated data. Journal of Engineering Mechanics, 2004. 130(1): p. 49-60.
[30] Dyke, S., Report on the Building Structural Health Monitoring Problem Phase 1 Experimental. 2011.
[31] Friedman, N., D. Geiger, and M. Goldszmidt, Bayesian network classifiers. Machine learning, 1997. 29(2): p. 131-163.
[32] Mahmoudi, H., et al. A rapid machine learning-based damage detection algorithm for identifying the extent of damage in concrete shear-wall buildings. in Structures. 2023. Elsevier.
[33] Al-Hegami, A.S., Classical and incremental classification in data mining process. Int. J. Comput. Sci. Netw. Security, 2007. 7(12): p. 179-187.
[34] Sá, A., et al. Lightning forecast using data mining techniques on hourly evolution of the convective available potential energy. in Brazilian Congress on Computational Intelligence, Fortaleza, November. 2011.
[35] Banaei, A., M. Salkhordeh, and S. Soroushian, An Optimized Machine Learning-Based Classification Algorithm for Identifying the Extent of Damage in Moment-Frame Buildings. Available at SSRN 4648192.
[36] Snoek, J., H. Larochelle, and R.P. Adams, Practical bayesian optimization of machine learning algorithms. Advances in neural information processing systems, 2012. 25.
[37] Guyon, I., et al., Feature extraction: foundations and applications. Vol. 207. 2008: Springer.
[38] Koch, H., Determining the effects of vibration in buildings. VDIZ, 1953. 25(21): p. 744-747.
[39] Rathje, E.M., et al., Empirical relationships for frequency content parameters of earthquake ground motions. Earthquake Spectra, 2004. 20(1): p. 119-144.
[40] Mirtaheri, M., M. Salkhordeh, and M. Mohammadgholiha, A System Identification-Based Damage-Detection Method for Gravity Dams. Shock and Vibration, 2021. 2021.
[41] Grossmann, A., J. Morlet, and T. Paul, Transforms associated to square integrable group representations. I. General results. Journal of Mathematical Physics, 1985. 26(10): p. 2473-2479.
[42] Lin, Y.z., Z.h. Nie, and H.w. Ma, Structural damage detection with automatic feature‐extraction through deep learning. Computer‐Aided Civil and Infrastructure Engineering, 2017. 32(12): p. 1025-1046.
[43] Fan, J., S. Upadhye, and A. Worster, Understanding receiver operating characteristic (ROC) curves. Canadian Journal of Emergency Medicine, 2006. 8(1): p. 19-20.
 
Numerical Methods in Civil Engineering
Volume 8, Issue 2 - Serial Number 2
December 2023
Pages 70-79

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History

  • Receive Date: 24 January 2023
  • Revise Date: 02 September 2023
  • Accept Date: 31 January 2024

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