Paper Titles
Bearing Fault Detection Using Higher-Order Statistics Based ARMA Model
p.271
Structural Health Monitoring on Real Scale Model of a Masonry Triumphal Arch
p.279
Rc Infilled Frame: Shaking Table Tests on a Full Scale Model
p.285
Damage Quantification and Monitoring in Masonry Monuments through Digital Photogrammetry
p.291
Experimental Seismic Damage Quantification in a 3-Storey Laboratory Structure
p.297
A Framework for Novelty Detection in Jet Engine Vibration Data
p.305
Structural Health Monitoring Experimental System Implemented in a Turboprop Commercial Aircraft
p.311
Effect of Crowning of Dovetail Joints on Turbine Blade Root Damping
p.317
Comparison of RBF Neural Network and Support Vector Machine on Aero-Engine Vibration Fault Diagnosis
p.323

Experimental Seismic Damage Quantification in a 3-Storey Laboratory Structure

Article Preview

Abstract:

Time series based Structural Health Monitoring (SHM) methods are being increasingly explored. In this study, Autoregressive (AR) models were used to fit the acceleration time histories of a 3-storey laboratory structure under excitation by earthquake records in several damaged and undamaged states. The coefficients of the AR models were used as inputs into an Artificial Neural Network (ANN) and the ANN was trained to relate the AR coefficients to the damage at each storey. The results showed that proposed method was able to detect, locate and quantify the damage in the structure with a very high accuracy.

You might also be interested in these eBooks
Damage Assessment of Structures VII View Preview

Info:

Periodical:

Key Engineering Materials (Volume 347)

Pages:

297-302

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.347.297

Citation:

Cite this paper

Online since:

September 2007

Authors:

Oliver R. de Lautour, Piotr Omenzetter

Keywords:

Artificial Neural Network (ANN), Autoregressive Models, Damage Detection, Earthquake Engineering, Structural Health Monitoring (SHM)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2007 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

References

[1] H. Sohn, J.A. Czarnecki and C.R. Farrar: Journal of Structural Engineering Vol. 125 (2000), p.1356.

Google Scholar

[2] P. Omenzetter and J.M.W. Brownjohn: Smart Materials and Structures Vol. 15 (2006). p.129.

Google Scholar

[3] O.R. de Lautour and P. Omenzetter: Proceedings of the 4th World Conference on Structural Control and Monitoring, July 11-13, San Diego, USA (2006) p.1 on CDROM.

Google Scholar

[4] O.R. de Lautour and P. Omenzetter: Proceedings of the 19th Australasian Conference on the Mechanics of Structures and Materials, November 29 - December 1 2006, Christchurch, New Zealand.

Google Scholar

[5] W.W.S. Wei: Time series analysis: Univariate and Multivariate Methods (Pearson, USA 2006).

Google Scholar

[6] H. Adeli: Computer-Aided Civil and Infrastructure Engineering Vol. 16 (2001), p.126.

Google Scholar

[7] D.E. Rumelhart, G.E. Hinton and R.J. Williams, in: Learning internal representations by error propagation edited by D.E. Rumelhart and J.L. McClelland, Parallel distributed processing: Explorations in the microstructures of cognition, p.318, MIT Press, (1986).

DOI: 10.7551/mitpress/4943.003.0042

Google Scholar

[8] M.T. Hagan and M. Menhaj: IEEE Transactions on Neural Networks Vol. 5 (1994), p.989.

Google Scholar

[9] MATLAB Signal Processing Toolbox (Mathworks, USA 2006).

Google Scholar