Journal of Theoretical
and Applied Mechanics
0, 0, pp. , Warsaw 0

Structural Damage Detection in Moving Load Problem using JRNNs based Method

SHAKTI PRASANNA JENA, DAYAL RAMAKRUSHNA PARHI
Damage detection in structure using vibration signature is quiet smart method for condition monitoring of structure. In this problem, Recurrent Neural Networks (RNNs) based method has been implemented for damage detection in moving load problem as inverse method. A multi-cracked simply supported beam under a traversing load has been considered for the present problem. The localization and severities of the supervised cracks on the structure are determined using the adapted Jordan’s Recurrent Neural Networks (JRNNs) approach. The mechanism of Levenberg-Marquardt’s back propagation algorithm has been implemented to train the networks. To check the adoptability of the proposed JRNNs method, numerical analyses along with laboratory test verifications have been conducted and found to be well emerged.
Keywords: JRNNs ;crack depth; crack location

References


Bandara, R. P, Chan, T, HT and Thambiratnam, D, P, 2014, Structural damage detection method using frequency response functions, Structural Health Monitoring, 13, 4, 418-429.

Bu, Q.J., Law, S.S. and Zhu, Q, X., 2006, Innovative Bridge Condition Assessment from Dynamic Response of a Passing Vehicle, Journal of Engineering Mechanics(ASCE), 132,1372-137.

Coban, R.,2013, A context layered locally recurrent neural network for dynamic system identification, Engineering Applications of Artificial Intelligence,26, 241-250.

Ettefagh, M.M., Akbari, H., Asadi, K. and Abbasi, F., 2014, New structural damage-identification method using modal updating and model reduction, Journal of Mechanical Engineering Science, 229,6,1041-1059.

Ekici, S., Yildirim, S. and Poyraz, M., 2009, A transmission line fault locator based on Elman recurrent networks, Applied Soft Computing, 9, 341-347.

Hu, X. and Balasubramaniam, P., 2008, Case Studies for Applications of Elman Recurrent Neural Networks”, Recurrent Neural Network, 357-378, InTech Publications, Shanghai, China

He, W-Y and Zhu, S., 2016, Moving load-induced response of damaged beam and its application in damage localization, Journal of Vibration and Control, 22,16, 3601-3617.

H. Yu and B. M. Wilamski, Levenberg-Marquardt Training, 2010,Lecture notes on intelligent systems, Auburn University.

Jena, S and Parhi D R,2017, Parametric Study on the Response of Cracked Structure Subjected to Moving Mass, Journal of Vibration Engineering & Technologies,5,1,11-19.

Jena, S., Parhi D R. and Mishra D., 2015, Comparative Study on Cracked Beam with Different types of cracks Carrying Moving Mass, Structural Engineering and Mechanics, International Journal, 56,5,797-811.

Jena, S and Parhi D R., 2017, Response analysis of cracked structure subjected to transit mass-a parametric study, Journal of Vibroengineering, 19, 5, 3243-3254.

Jena, S and Parhi D R., 2018, Dynamic response and analysis of cracked beam subjected to transit mass, International Journal of Dynamics and Control, 6,3,961-972.

Kong, X., Cai, C.S. and Kong, B., 2015, Damage Detection Based on Transmissibility of a Vehicle and Bridge Coupled System, Journal of Engineering Mechanics (ASCE), 114, 1, 1-17.

Lee, J. J, Lee, J.W., Yi, J H., Yun, B.C and Jung, Y.H., 2005, Neural networks-based damage detection for bridges considering errors in baseline finite element models, Journal of Sound and Vibration, 280, 3,555-578.

Li, J. and Law, S.S., 2012, Damage identification of a target substructure with moving load excitation, Mechanical Systems and Signal Processing, 30, 78-90.

Li, J, Law, S.S and Hao, H, 2013, Improved damage identification in bridge structures subject to moving loads: Numerical and experimental studies, International Journal of Mechanical Sciences, 74, 99-111.

Lee, L-T. and Wu, J-Y., 2014, Identification of crack locations and depths in a multi-cracks beam by using local adaptive differential quadrature method, Journal of Process Mechanical Engineering, 229, 4,243-255.

Malhi, A. and Gao, X. R., 2004, Recurrent Neural Networks for Long-Term Prediction in Machine Condition Monitoring”, IMTC 2004, Coma, Italy, 18-20 May 2004.

Mehrjooa, M., Khaji, N., Moharrami, H. and Bahreininejad, A., 2008, Damage detection of truss bridge joints using Artificial Neural Networks, Expert Systems with Applications, 35,3, 1122-1131.

Mousavi, M and Gandomi, A H., 2018, An input–output damage detection method using static equivalent formulation of dynamic vibration, Archives of Civil and Mechanical Engineering,18,2, 508-514.

Nandakumar, P. and Shankar, K., 2014, Multiple crack damage detection of structures using the two crack transfer matrix, Structural Health Monitoring, 13, 5, 548-561.

Oshima, Y., Yamamoto, K. and Sugiura, K., 2014, Damage assessment of a bridge based on mode shapes estimated by responses of passing vehicles, Smart Structures and Systems,13, 5,731-753.

Owolabi, G.M., Swamidas, A.S.J. and Seshadri, R., 2003, “Crack detection in beams using changes in frequencies and amplitudes of frequency response functions”, Journal of Sound and Vibration, 265, 1-22.

Pakrashi, V., O’Connor, A. and Basu, B., 2010, A Bridge-Vehicle Interaction Based Experimental Investigation of Damage Evolution, Structural Health Monitoring, 9, 4, 285-296.

Perez, C. G. and Gonzalez, V, J., 2011, Identification of Structural Damage in a Vehicular Bridge using Artificial Neural Networks, Structural Health Monitoring, 10,1, 33-16.

Parhi, D R and Jena, S., 2017, Dynamic and experimental analysis on response of multiple cracked structures carrying transit mass, Journal of Risk and Reliability,231,1,25-35.

Piatkowska and Guminiak,2017,Crack identification in plates using 1-D discrete wavelet transform, Journal of Theoretical and Applied Mechanics, 55, 2,481-496.

Rucka, M and Wilde, K., 2010, Neuro-wavelet damage detection technique in Beam, plate and shell structures with experimental validation, Journal of Theoretical and Applied Mechanics,48,3,579-604.

Seker, S., Ayaz, E. and Turkcan, E., 2003, Elman’s recurrent neural network applications to condition monitoring in nuclear power plant and rotating machinery, Engineering Applications of Artificial Intelligence, 16,647-656.

Sayyad, B, F and Kumar, B, 2011, Identification of crack location and crack size in a simply supported beam by measurement of natural frequencies, Journal of Vibration and Control,18,2,183-190.

Vosough, A. R., 2015, A developed hybrid method for crack identification of beams, Smart structure and System, 16, 3,401-414.

Yeung, T.W. and Smith, W.J., 2005, Damage detection in bridges using neural networks for pattern recognition of vibration signatures, Engineering Structures, 27, 5, 685-698.

Yan, J.Y., Cheng, L., Wu, Y, Z. and Yam, H.L., 2007, Development in vibration-based structural damage detection technique, Mechanical Systems and Signal Processing, 21,2198-2211.