Abstract
This paper presents the use of Artificial Neural Networks (ANN) techniques to identify the damage in cantilever beams. We consider two cantilever beams of different materials i.e. aluminum and stainless steel. Different crack lengths are introduced on the beams from 0–10 mm with 2 mm interval. 0 mm, 2 mm, 4 mm, 6 mm, 8 mm and 10 mm cracks denotes damage level 0, 1, 2, 3, 4 and 5 respectively. The undamaged cantilever structure is treated as damage level 0. Experimental modal analysis is conducted for each case using impact hammer test. To validate the experimental values modal analysis is conducted in ANSYS software. From the modal analysis results it is observed that, for lower modes there is no change in frequencies but for higher modes the natural frequencies are decreasing with the increase in crack length. The FRFs obtained from experimental modal analysis are used as inputs to train the ANN. In the present paper, two types of networks are considered. One is Radial Basis Function (RBF) network and other is feed forward network. For each material total of 60 sets of data were collected. Part of the data is used to train the ANN and remaining data is used to test the trained ANN. From the results, the ANN is capable of identifying the damage and its severities.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rao, P.S., Ratnam, Ch.: Vibration based damage identification using Burg’s algorithm and Shewhart charts. J. ASTM Int. 8(4), 1–12 (2011)
Rao, P.S., Ratnam, Ch.: Health monitoring of welded structures using stastical process control. Mech. Syst. Signal Process. 27, 683–69512 (2012)
Rytter, A.: Vibration based inspection of civil engineering structures. PhD thesis, Aalborg University, Aalborg (1993)
Cawley, P., Adams, R.D.: The location of defects in structures from measurements of natural frequencies. J. Strain Anal. 14(2), 49–57 (1979)
Sahin, M., Shenoi, R.A.: Quantification and localization of damage in beam-like structures by using artificial neural networks with experimental validation. Eng. Struct. 25, 1785–1802 (2003)
Lee, J.J., Yun, C.B.: Damage diagnosis of steel girder bridges using ambient vibration data. J. Eng. Struct. 28, 912–925 (2006)
Mayes, I.W., Davies, W.G.R.: Analysis of the response of a multi-rotor-bearing system contacting a transverse crack in a rotor. J. Vib. Acoust. Stress Reliab. Des. ASME 106(1), 139–145 (1984)
Wu, X., Ghaboussi, J., Garret, J.H.: Use of neural networks in detection of structural damage. Comput. Struct. 41(4), 649–659 (1992)
Povich, C., Lim, T.W.: An artificial neural network approach to structural damage detection using frequency response function. In: Proceeding of the AIAA Adaptive Structures Forum, USA (1994)
Ghate, V.N., Dudul, S.V.: Cascaded neural-network based fault classifier for three phase induction motor. IEEE Trans. Ind. Electron. 58(5), 1555–1563 (2011)
Liu, P., Sana, S., Rao, V.S.: Structural damage identification using time-domain parameter estimation techniques. Structural health monitoring 2000, pp. 812–820. Stanford University, Palo Alto (1999)
Narkis, Y.: Identification of crack location in vibrating simply supported beams. J. Sound Vib. 172(4), 549–558 (1994)
Masri, S.F., Nakamura, M., Chassiakos, A.G., Caughey, T.K.: Neural network approach to detection of changes in structural parameters. J. Eng. Mech. ASCE 126(7), 666–676 (1996)
Rao, P.S., Ratnam, Ch.: Experimental and analytical modal analysis of welded structure used for vibration based damage identification. Glob. J. Res. Eng. Mech. Mech. Eng. 12(1), 631–642 (2012)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Rao, P.S., Mahendra, N.V.D. (2018). Vibration Based Damage Identification Method for Cantilever Beam Using Artificial Neural Network. In: Conte, J., Astroza, R., Benzoni, G., Feltrin, G., Loh, K., Moaveni, B. (eds) Experimental Vibration Analysis for Civil Structures. EVACES 2017. Lecture Notes in Civil Engineering , vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-67443-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-67443-8_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67442-1
Online ISBN: 978-3-319-67443-8
eBook Packages: EngineeringEngineering (R0)