Damage Identification of Structures Using Second-Order Approximation of Neumann Series Expansion

Document Type: Regular Paper

Author

Department of Civil Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran

Abstract

In this paper, a new method proposed for structural damage detection from limited number of sensors using extreme learning machine (ELM). One of the main challenges in structural damage identification problems is the limitation in the number of used sensors. To address this issue, an effective model reduction method has been proposed. To condense mass and stiffness matrices, the second-order approximation of Neumann series expansion (NSEMR-II) has been used. Mode shapes and frequencies of damaged structures and corresponding generated damage states used as input and output to train extreme learning machine, respectively. To show the effectiveness of presented method, three different examples consists of a truss structure, irregular frame and shear frame have been studied. The obtained results show the ability of the proposed approach in identifying and estimating different damage cases using limited numbers of installed sensors and noisy modal data. 

Keywords

Main Subjects

References

[1] Fan, W., Qiao, P. (2011). “Vibration-based damage identification approachs: a review and comparative study.” Structural Health Monitoring, Vol.10, pp. 83-111.

[2] Kourehli, S.S. (2015). “Damage quantification approach using artificial neural network and static response with limited sensors.” Journal of Vibroengineering, Vol. 17, pp. 1317‑1325.

[3] Ghannadi, P., & Kourehli, S. S. (2018). “Investigation of the accuracy of different finite element model reduction techniques.” Structural Monitoring and Maintenance, Vol. 5, pp. 417-428.

[4] Hosseinzadeh, A.Z., Bagheri, A., Ghodrati Amiri, G. and Koo, K.Y. (2014). “A flexibility-based approach via the iterated improved reduction system and the cuckoo optimization algorithm for damage quantification with limited sensors.” Smart Materials and Structures, Vol. 23, 045019, doi: 10.1088/0964-1726/23/4/045019.

[5] Kourehli. S.S., Bagheri, A., Ghodrati Amiri, G., Ghafory-Ashtiany, M. (2014). “Structural damage identification approach based on incomplete static responses using an optimization problem.” Scientia Iranica, Vol. 21, pp. 1209-1216.

[6] Li, H., Wang, J. and Hu, SLJ. (2008). “Using incomplete modal data for damage detection in offshore jacket structures.” Ocean Eng. Vol. 35, pp. 1793–9.

[7] Rasouli, A., Ghodrati Amiri, G., Kheyroddin, A., Ghafory-Ashtiany, M. and Kourehli, S.S. (2014). “A New Approach for Damage Prognosis Based on Incomplete Modal Data via an Evolutionary Algorithm.” European Journal of Environmental and Civil Engineering, Vol. 18, pp. 253-270.

[8] Au, FTK., Cheng, Y.S., Tham, L.G. and Bai ZZ. (2003). “Structural damage detection based on a microgenetic algorithm using incomplete and noisy modal test data.” J. Sound Vib., Vol. 259, pp. 1081–94.

[9] Ghannadi, P., & Kourehli, S. S. (2019). “Data-driven method of damage detection using sparse sensors installation by SEREPa.” Journal of Civil Structural Health Monitoring, Vol. 9, pp. 459-475.

 [10] Djemana, M., Hrairi, M., and Al Jeroudi, Y. (2017). “Using Electromechanical Impedance and Extreme Learning Machine to Detect and Locate Damage in Structures. “ Journal of Nondestructive Evaluation. Vol. 36, pp. 39.

[11] Gökdağ, H. (2013). “A Crack Identification Approach for Beam-Like Structures under Moving Vehicle using Particle Swarm Optimization.” Materials Testing., Vol. 55, pp. 114-120.

[12] Kaveh, A., Hoseini Vaez, S.R., Hosseini, P. (2017), “Enhanced vibrating particles system algorithm for damage identification of truss structures”, Scientia Iranica, Transactions on Civil Engineering, DOI: 10.24200/SCI.2017.4265

[13] Hoseini Vaez, S.R., Fallah, N. (2017), “Damage detection of thin plates using GA-PSO algorithm based on modal data”, Arabian Journal for Science and Engineering, Vol. 42, Issue 3, pp. 1251 1263.

[14] Kaveh, A., Hoseini Vaez, S.R., Hosseini, P., Fallah, N. (2016), “Detection of damage in truss structures using Simplified Dolphin Echolocation algorithm based on modal data”, Smart Structures and Systems, Vol. 18, No. 5, pp. 983-1004.

[15] Hoseini Vaez, S.R., Arefzade, T. (2017), “Vibration-based damage detection of concrete gravity dam monolith via wavelet transform”, Journal of Vibroengineering, Vol. 19, Issue 1, pp. 204 213.

[16] Hoseini Vaez, S.R., Dehghani, E., Babaei, V. (2017), “Damage Detection in Post-tensioned Slab Using 2D Wavelet Transforms”, Journal of Rehabilitation in Civil Engineering, Vol. 5, Issue 2, pp. 25-38.

[17] Bagheri, A., Kourehli, S.S (2013), “Damage detection of structures under earthquake excitation using discrete wavelet analysis” Asian journal of civil engineering (BHRC), Vol. 14, N0. 2., pp. 289-304.

[18] Yazdanpanah, O., Seyedpoor, S. M. and Akbarzadeh Bengar, H. (2015), “A new damage detection indicator for beams based on mode shape data”, Structural Engineering and Mechanics, Vol. 53, pp. 725-744. 

[19] Naderpour H. and Fakharian P. (2016), “A synthesis of peak picking method and wavelet packet transform for structural modal identification”, KSCE Journal of Civil Engineering, Vol. 20, Issue 7, pp. 2859–2867.

[20] Kourehli, S. S. (2017). “Application of extreme learning machine to damage detection of plate-like structures. “International Journal of Structural Stability and Dynamics, Vol. 17, 1750068.

[21] Kourehli, S.S. (2015). “Damage assessment in structures using incomplete modal data and artificial neural network.” International Journal of Structural Stability and Dynamics. 15: 1450087. doi: 10.1142/S0219455414500874.

[22] Ghadimi, S., Kourehli, S.S. (2017). “Multiple Crack Identification in Euler Beams Using Extreme Learning Machine.” KSCE journal of civil engineering , doi: 10.1007/s12205-016-1078-0.

[23] Guyan, R.J. (1965). “Reduction of stiffness and mass matrices.” AIAA journal, Vol. 3, pp. 380-380.

[24] Yang, Q.W. (2009). “Model reduction by Neumann series expansion.” Applied Mathematical Modelling, Vol. 33, pp. 4431-4434.

[25] Sauer, G. (1989). “Iterative improvement of eigensolutions from reduced matrices.” Communications in Applied Numerical Approachs, Vol. 5, pp. 329-335.

[26] Zhang, N. (1995). “Dynamic condensation of mass and stiffness matrices.” Journal of Sound and Vibration, Vol. 188, pp. 601-615.

[27] Bouhaddi, N., Fillod, R. (1996). “Model reduction by a simplified variant of dynamic condensation.” Journal of Sound and Vibration, Vol. 191, pp. 233-250.

[28] Pang, J., Dukkipati, R., Patten, W.N. and Sheng, G. (2003). “Comparative analysis of model-reduction approachs.” International Journal of Heavy Vehicle Systems, Vol. 10, pp. 224-253.

[29] Huang, G.B., Zhu, Q.Y., Siew, C.K. (2006). “Extreme Learning Machine: A New Learning Scheme of Feed forward Neural Networks.” Neurocomputing, Vol. 70, pp. 489-501.

[30] MATLAB. (2015). Matlab user manual. Lowell (MA): Mathwork.

[31] Zare, Hosseinzadeh, Ghodrati Amiri, G., & Seyed Razzaghi, S. A. (2017). “Model-based identification of damage from sparse sensor measurements using Neumann series expansion.” Inverse Problems in Science and Engineering, Vol. 25, pp. 239-259.

Files

History

  • Receive Date: 10 December 2017
  • Revise Date: 11 June 2018
  • Accept Date: 12 June 2018

Share

How to cite

Statistics