Machine learning algorithms for damage detection under operational and environmental variability

Abstract

The goal of this article is to detect structural damage in the presence of operational and environmental variations using vibration-based damage identification procedures. For this purpose, four machine learning algorithms are applied based on the auto-associative neural network, factor analysis, Mahalanobis distance, and singular value decomposition. A base-excited three-story frame structure was tested in laboratory environment to obtain time-series data from an array of accelerometers under several structural state conditions. Tests were performed with varying stiffness and mass conditions with the assumption that these sources of variability are representative of changing operational and environmental conditions. Damage is simulated through nonlinear effects introduced by a bumper mechanism that induces a repetitive, impact-type nonlinearity. This mechanism intends to simulate the cracks that open and close under dynamic loads or loose connections that rattle. The unique contribution of this study is a direct comparison of the four proposed machine learning algorithms that have been reported as reliable approaches to separate structural conditions with changes resulting from damage from changes caused by operational and environmental variations.

Keywords

SHM damage detection operational and environmental variations ROC curves

Get full access to this article

View all access options for this article.

References

Farrar

Doebling

Nix

. Vibration-based structural damage identification. Philos Trans R Soc A 2001; 356(1778): 131–149.

Worden

Farrar

Manson

Park

. The fundamental axioms of structural health monitoring. Proc R Soc A 2007; 463(2082): 1639–1664.

Farrar

Worden

. An introduction to structural health monitoring. Philos Trans R Soc 2007; 365: 303–315.

Sohn

Worden

Farrar

. Statistical damage classification under changing environmental and operational conditions. J Intell Mater Syst Struct 2002; 13(9): 561–574.

Fawcett

. ROC graphs: notes and pratical considerations for researchers, Palo Alto, CA: HP Laboratories, 2004.

Lynch

Loh

. A summary review of wireless sensors and sensor networks for structural health monitoring. Shock Vib Digest 2006; 38(2): 91–128.

Kantz

Schreiber

. Nonlinear time series analysis, Cambridge: Cambridge University Press, 1997.

Pimentel M, Santos J and Figueiras J. Safety appraisal of an existing bridge via detailed modelling. In: International FIB Symposium 2008: Tailor Made Concrete Solutions, New Solutions for our Society, Amsterdam, Netherlands, 2008.

Kim CY, Jung DS, Kim NS and Yoon GJ. Effect of vehicle mass on the measured dynamic characteristics of bridges from traffic-induced test. In: Proceedings of the 19th International Modal Analysis Conference (IMAC XIX), Kissimmee, FL, 1999, pp.1106–1110.

10.

Soyoz

Feng

. Long-term monitoring and identification of bridge structural parameters. Comput Aided Civ infrastruct Eng 2009; 24: 82–92.

11.

. Technology developments in structural health monitoring of large-scale bridges. Eng Struct 2005; 27: 1715–1725.

12.

Peeters

Maeck

De Roeck

. Vibratio-based damage detection in civil engineering: excitation sources and temperature effects. Smart Mater Struct 2001; 10: 518–527.

13.

Farrar CR, Cornwell PJ, Doebling SW and Prime MB. Structural health monitoring studies of the Alamosa Canyon and I-40 bridges. Los Alamos National Laboratory Report: LA-13635-MS, 2000.

14.

Farrar CR, Baker WE, Bell TM, et al. Dynamic characterization and damage detection in the I-40 bridge over the Rio Grand. Los Alamos National Laboratory Report: LA-12767-MS, 1994.

15.

Alampalli S. Influence of in-serive environment on modal parameters. In: Proceedings of IMAC XVI, February 2–5, Santa Barbara, CA, 1998, pp. 111–116.

16.

Kullaa J. Is temperature measurement essential in structural health monitoring? In: Proceedings of the 4th International Workshop on Structural Health Monitoring 2003: From Diagnostic & Prognostics to Structural Health Monitoring, DEStech Publications, Inc., USA, 2003, pp.717–724.

17.

Worden

Manson

. Damage detection using outlier analysis. J Sound Vib 2000; 229(3): 647–667.

18.

Ruotolo

Surage

. Using SVD to detect damage in structures with different operational conditions. J Sound Vib 1999; 226(3): 425–439.

19.

Worden

Sohn

Farrar

. Novelty detection in a changing environmental: regression and interpolation approaches. J Sound Vib 2002; 258(4): 741–761.

20.

Hsu

T-Y

Loh

C-H

. Damage detection accommodating nonlinear environmental effects by nonlinear principal component analysis. Struct Contr Health Monit 2010; 17: 338–354.

21.

Yan

A-M

Kerschen

Boe

Golinval

J-C

. Structural damage diagnosis under varying environmental conditions – Part I: a linear analysis. Mech Syst Sig Process 2005; 19: 847–864.

22.

Yan

A-M

Kerschen

Boe

Golinval

J-C

. Structural damage diagnosis under varying environmental conditions – Part II: local PCA for non-linear cases. Mech Syst Sig Process 2005; 19: 865–880.

23.

Worden

Manson

. The application of machine learning to structural health monitoring. Philos Trans R Soc A 2007; 365: 515–537.

24.

Sohn H, Farrar C, Hunter N and Worden K. Applying the LANL statistical pattern recognition paradigm for structural health monitoring to data from a surface-effect fast patrol boat. Los Alamos National Laboratory Report: LA-13761-MS, 2001.

25.

Omenzetter

Brownjohn

. Application of time series analysis for bridge monitoring. Smart Mater Struct 2006; 15: 129–138.

26.

Box

Jenkins

Reinsel

. Time series analysis: forecasting and control, 3rd ed. New Jersey: Prentice-Hall, Inc, 1994.

27.

Kothamasu

Shi

Huang

Leep

. Comparison of selected model evaluation criteria for maintenance applications. Struct Health Monit 2004; 3(3): 213–224.

28.

Kramer

. Nonlinear principal component analysis using autoassociative neural networks. AIChE J 1991; 37(2): 233–243.

29.

Sharma

. Applied multivariate techniques, New York: John Wiley & Sons, 1995.

30.

Elsner

Tsonis

. Singular spectrum analysis: a new tool in time series analysis, New York: Plenum Press, 1996.

31.

Figueiredo E, Park G, Figueiras J, Farrar C and Worden K. Structural health monitoring algorithm comparisons using standard data sets. Los Alamos National Laboratory Report: LA-14393, 2009.