Response Surface Models to Detect and Localize Distributed Cracks in a Complex Continuum
Publication: Journal of Engineering Mechanics
Volume 136, Issue 9
Abstract
Linear response surface (RS) models are used to represent the relationship between samples of response time histories measured by sensors placed across a structure. Different structural states of a general time-variant system are considered for short intervals capturing a linearized model of each state. Within this framework, the error associated with each RS model is sensitive to a modification of the structural state. A method that relates the changes of the statistical characterization of the error to the occurrence of a structural modification is developed for damage detection. The localization of damage is then pursued by identifying the largest discrepancies resulting from the comparison between the statistics of the sum of the squares of the error obtained at each sensor location. The generality of the method is shown by applying it to the experimental data of a realistic structure, which is representative of a continuous body affected by distributed cracking.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This research was supported by grants from the Athenaeum Research Fund of the University of Catania (PRA 2007) and from the Italian Ministry of University Research Fund (PRIN) of the University of Pavia, where the writer previously served as a postdoctoral researcher.
References
Armelle, A. (1997). “Final report. POP Sicilia.” Rep. No. E10c, Joint Research Centre of European Commission, Ispra, Italy.
Basseville, M., Abdelghani, M., and Benveniste, A. (1997). “Subspace-based fault detection and isolation—Application to vibration monitoring.” Rep. No. 3299, Institut National de Recherche en Informatique et en Automatique, Rennes, France.
Beni, F. (2002). “Indagini diagnostiche e tecniche di identificazione strutturale per la conservazione dei monumenti.” MS thesis, Univ. of Genova, Genova, Italy (in Italian).
Beni, F., Lagomarsino, S., Marazzi, F., Magonette, G., and Podestà, S. (2003). “Structural monitoring through dynamic identification.” Proc., 3rd World Conf. on Structural Control, Wiley, Chichester, U.K., 139–146.
Bernal, D. (2002). “Load vectors for damage localization.” J. Eng. Mech., 128(1), 7–14.
Breitung, K., and Faravelli, L. (1996). “Response surface methods and asymptotic approximations.” Mathematical models for structural reliability analysis, Chap. 6, F. Casciati and J. B. Roberts, eds., CRC, Boca Raton, Fla., 227–285.
Casciati, F., and Casciati, S. (2006). “Structural health monitoring by Lyapunov exponents of non-linear time series.” Struct. Control Health Monit., 13, 132–146.
Casciati, S. (2004). “Statistical models comparison for damage detection using the ASCE benchmark.” Proc., Structural Health Monitoring 2004, DEStech, Lancaster, Pa., 695–702.
Casciati, S. (2005). “Damage detection and localization in the space of the observed variables.” Ph.D. thesis, Graduate School of Civil Engineering, Univ. of Pavia, Pavia, Italy.
Casciati, S. (2008). “Stiffness identification and damage localization via differential evolution algorithms.” Struct. Control Health Monit., 15, 436–449.
Casciati, S., Colabrese, E., and Magonette, G. (2003). “Monitoring and response surface methodology to detect and locate structural damage.” Proc., 1st Int. Conf. on Structural Health Monitoring and Intelligent Infrastructure, Balkema, Swets & Zeitlinger B.V., Lisse, The Netherlands, 423–429.
Casciati, S., Domaneschi, M., and Inaudi, D. (2005). “Local damage detection from dynamic SOFO experimental data.” Proc., Smart Structures and Materials 2005: Sensors and Smart Structure Technologies for Civil, Mechanical and Aerospace Systems, SPIE, Bellingham, Wash., 591–599.
Casciati, S., and Rossi, R. (2006). “Embedding SHM algorithms into a microcontroller for real-time and fully-automated civil applications.” Proc., Structural Health Monitoring 2006, DEStech, Lancaster, Pa., 539–546.
Draper, N. R., and Smith, H. (1981). Applied regression analysis, Wiley, New York, 87.
Faravelli, L., and Casciati, S. (2004). “Structural damage detection and localization by response change diagnosis.” Prog. Struct. Eng. Mater., 6(2), 104–115.
Guemes, A., ed. (2006). “Structural Health Monitoring 2006.” Proc., 3rd European Workshop Structural Health Monitoring 2006, DEStech, Lancaster, Pa.
Iwasaki, A., Todorokim, A., and Sugiya, T. (2002). “Remote smart damage detection via internet with unsupervised statistical diagnosis.” Proc., IUTAM Symp. on Dynamics of Advanced Materials and Smart Structures, Kluwer, Dortrecht, The Netherlands, 157–166.
Johnson, E. A., Lam, H. F., Katafygiotis, L. S., and Beck, J. L. (2004). “Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data.” J. Eng. Mech., 130(1), 3–15.
Juang, J. N. (1992). Applied system identification, Prentice-Hall, Englewood Cliffs, N.J.
Kullaa, J. (2004). “Structural health monitoring under variable environmental or operational conditions.” Proc., Structural Health Monitoring 2004, DEStech, Lancaster, Pa., 1262–1269.
Kullaa, J. (2006). “Removing non-linear environmental influences from structural features.” Proc., Structural Health Monitoring 2006, DEStech, Lancaster, Pa., 565–573.
Ljung, L. (1999). System identification—Theory for the user, 2nd Ed., Prentice-Hall, Upper Saddle River, N.J.
Masri, S. F., Smyth, A. W., Chassiakos, A. G., Caughey, T. K., and Hunter, N. F. (2000). “Application of neural networks for detection of changing in nonlinear systems.” J. Eng. Mech., 126(7), 666–683.
Mathworks Inc. (1992). MATLAB reference guide, Natick, Mass.
Information & Authors
Information
Published In
Copyright
© 2010 ASCE.
History
Received: Nov 8, 2009
Accepted: Feb 12, 2010
Published online: Feb 15, 2010
Published in print: Sep 2010
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.