Since 1963
ARTICLE
Structural damage detection in moving load problem using JRNNs based method
1
Department of Mechanical Engineering, Vardhaman College of Engineering, Hyderabad, India
2
Department of Mechanical Engineering, National Institute of Technology, Rourkela, India
Submission date: 2018-11-08
Acceptance date: 2019-03-25
Online publication date: 2019-07-15
Publication date: 2019-07-15
Journal of Theoretical and Applied Mechanics 2019;57(3):665-676
KEYWORDS
ABSTRACT
Damage detection in a structure using the vibration signature is a quiet smart method
for condition monitoring of the structure. In this problem, the Recurrent Neural Networks
(RNNs) based method has been implemented for damage detection in the moving load problem
as an 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.
REFERENCES (35)
1.
Bandara R.P., Chan T.H.T., Thambiratnam D.P., 2014, Structural damage detection method using frequency response functions, Structural Health Monitoring, 13, 4, 418-429.
2.
Bu Q.J., Law S.S., Zhu Q,X., 2006, Innovative bridge condition assessment from dynamic response of a passing vehicle, Journal of Engineering Mechanics, ASCE, 132, 1372-1377.
3.
Coban R., 2013, A context layered locally recurrent neural network for dynamic system identification, Engineering Applications of Artificial Intelligence, 26, 241-250.
4.
Dems K., Mróz Z., 2001, Identification of damage in beam and plate structures using parameter dependent frequency changes, Engineering Computation, 18, 1/2, 96-120.
5.
Ekici S., Yildirim S., Poyraz,, M., 2009, A transmission line fault locator based on Elman recurrent networks, Applied Soft Computing, 9, 341-347.
6.
Ettefagh M.M., Akbari H., Asadi K., Abbasi F., 2014, New structural damage-identification method using modal updating and model reduction, Journal of Mechanical Engineering Science, 229, 6, 1041-1059.
7.
González-Pérez C., Valdés-González J., 2011, Identification of structural damage in a vehicular bridge using artificial neural networks, Structural Health Monitoring, 10, 1, 33-48.
8.
He W.-Y., 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.
9.
Hu X., Balasubramaniam P., 2008, Case studies for applications of Elman recurrent neural networks, Recurrent Neural Network, 357-378, InTech Publications, Shanghai, China.
10.
Jena S., Parhi D.R., 2017a, Dynamic response and analysis of cracked beam subjected to transit mass, International Journal of Dynamics and Control, DOI: 10.1007/s40435-017-0361-3.
11.
Jena S., Parhi D.R., 2017b, Parametric study on the response of cracked structure subjected to moving mass, Journal of Vibration Engineering and Technologies, 5, 1, 11-19.
12.
Jena S., Parhi D.R., 2017c, Response analysis of cracked structure subjected to transit mass - a parametric study, Journal of Vibroengineering, 19, 5, 3243-3254.
13.
Jena S., Parhi D.R., 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.
14.
Jena S., Parhi D.R., Subbaratnam B., 2017, Parametric evaluation on the response of damaged simple supported structure under transit mass, ASME 2017 Gas Turbine India Conference.
15.
Kong X., Cai C.S., Kong B., 2015, Damage detection based on transmissibility of a vehicle and bridge coupled system, Journal of Engineering Mechanics, ASCE, 114, 1, 1-17.
16.
Krawczuk M., Ostachowicz W., 1995, Modeling and vibration analysis of a cantilever composite beam with a transverse open crack, Journal of Sound and Vibration, 183, 1, 69-89.
17.
Lee J.J., Lee J.W., Yi J.H., Yun B.C., Jung Y.H., 2005, Neural networks-based damage detection for bridges considering errors in baseline finite element models, Journal of Sound and Vibration, 280, 555-578.
18.
Lee L.-T., 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.
19.
Li J., Law S.S., 2012, Damage identification of a target substructure with moving load excitation, Mechanical Systems and Signal Processing, 30, 78-90.
20.
Li J., Law S.S., 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.
21.
Malhi A., Gao X.R., 2004, Recurrent neural networks for long-term prediction in machine condition monitoring, IMTC 2004, Como, Italy.
22.
Mehrjooa M., Khaji N., Moharrami H., Bahreininejad A., 2008, Damage detection of truss bridge joints using Artificial Neural Networks, Expert Systems with Applications, 35, 3, 1122-1131.
23.
Mousavi M., 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.
24.
Nandakumar P., Shankar K., 2014, Multiple crack damage detection of structures using the two crack transfer matrix, Structural Health Monitoring, 13, 5, 548-561.
25.
Oshima Y., Yamamoto K., 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.
26.
Owolabi G.M., Swamidas A.S.J., 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.
27.
Pakrashi V., O'Connor A., Basu B., 2010, A bridge-vehicle interaction based experimental investigation of damage evolution, Structural Health Monitoring, 9, 4, 285-296.
28.
Parhi D.R., 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.
29.
Sayyad B.F., 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.
30.
Seker S., Ayaz E., 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.
31.
Thomson T.W., 1988, Theory of Vibration with Application, Third Edition, CBS Publishers & Distributors, New Delhi, India.
32.
Vosough A.R., 2015, A developed hybrid method for crack identification of beams, Smart structure and System, 16, 3, 401-414.
33.
Yan J.Y., Cheng L., Wu Y.Z., Yam H.L., 2007, Development in vibration-based structural damage detection technique, Mechanical Systems and Signal Processing, 21, 2198-2211.
34.
Yeung T.W., Smith, W.J., 2005, Damage detection in bridges using neural networks for pattern recognition of vibration signatures, 27, 5, 685-698.
35.
Yu H., Wilamowski B.M., 2011, Levenberg-Marquardt Training, The Industrial Electronics Handbook, Intelligent Systems, 2nd Edition, 12, (1-16), CRC Press, New York, US.
| eISSN: | 2543-6309 |
| ISSN: | 1429-2955 |
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