Damage Identification of Bolt Connections in a Steel Frame
Publication: Journal of Structural Engineering
Volume 140, Issue 3
Abstract
It is well-known that damage in a structure is a local phenomenon. Based on measured vibration data from sensors, the detection of a local structural damage requires the finite-element formulation for the equations of motion, so that any change in stiffness in a structural element can be identified. However, the finite-element model (FEM) of a complex structure involves a large number of degrees of freedom (DOF), which requires a large number of sensors and involves a heavy computational effort for the identification of structural damages. To overcome such a challenge, we propose the application of a reduced-order model in conjunction with a recently proposed damage detection technique, referred to as the adaptive quadratic sum-square error with unknown inputs (AQSSE-UI). Experimental data for the shake table tests of a -scale six-story steel frame structure, in which damages of the joints were simulated by loosening the connection bolts, have been available recently. Based on these experimental data, it is demonstrated that the proposed combination of the reduced-order finite-element model and the adaptive quadratic sum-square error with unknown inputs is quite effective for the damage assessment of joints in the frame structure.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This paper is supported by the U.S. National Science Foundation Grant No. NSF-CMMI-0853395 and Taiwan National Science Council Grant No. NSC96-2221-E-002-121-MY3.
References
Alvin, K. F., Robertson, A. N., Reich, G. W., and Park, K. C. (2003). “Structural system identification: from reality to models.” Comput. Struct., 81(12), 1149–1176.
Bernal, D., and Beck, J. (2004). “Special section: Phase I of the IASC-ASCE structural health monitoring benchmark.” J. Eng. Mech., 1–127.
Chang, F. K., ed. (2005). “Structural health monitoring.” Proc., 5th Int. Workshop on Structural Health Monitoring, DEStech Publ., Lancaster, PA.
Ching, J., Beck, J. L., Porter, K. A., and Shaikhutdinov, R. (2006). “Bayesian state estimation method for nonlinear systems and its application to recorded seismic response.” J. Eng. Mech., 396–410.
Chopra, A. K. (2001). “Dynamics of structures.” Theory and Applications to Earthquake Engineering, Prentice-Hall, Upper Saddle River, NJ.
Doebling, S. W., Farrar, C. R., and Prime, M. B. (1998). “A summary review of vibration-based damage identification methods.” Shock Vib. Digest, 30(2), 91–105.
Goodwin, C. G., and Sin, K. S. (1984). “Adaptive filtering prediction and control.” Inf. Syst. Sci. Ser.
Hoshiya, M., and Saito, E. (1984). “Structural identification by extended Kalman filter.” J. Eng. Mech., 1757–1771.
Huang, H., and Yang, J. N. (2008). “Damage identification of substructure for local health monitoring.” Smart Struct. Syst., 4(6), 795–807.
Huang, H., Yang, J. N., and Zhou, L. (2010). “Adaptive quadratic sum-squares error with unknown inputs for damage identification of structures.” Struct. Control Health Monit., 17(4), 404–426.
Jaspart, J. P. (2000). “General report: Session on connections.” J. Constr. Steel Res., 55(1–3), 69–89.
Lin, J. W., Betti, R., Smyth, A. W., and Longman, R. W. (2001). “On-line identification of nonlinear hysteretic structural systems using a variable trace approach.” Earthquake Eng. Struct. Dyn., 30(9), 1279–1303.
Lin, S., Yang, J. N., and Zhou, L. (2005). “Damage identification of a benchmark building for structural health monitoring.” Smart Mater. Struct., 14(3), 162–169.
Loh, C. H., Lin, C. Y., and Huang, C. C. (2000). “Time domain identification of frames under earthquake loadings.” J. Eng. Mech., 693–703.
Mevel, L., and Basseville, M. (2003). “Stochastic subspace-based structural identification and damage detection, application to the steel-quake benchmark.” Mech. Syst. Signal Process., 17(1), 91–101.
Nethercot, D. A. (2000). “Frame structures, global performance, static and stability behavior, general report.” J. Constr. Steel Res., 55(1–3), 109–124.
Sato, T., Honda, R., and Sakanoue, T. (2001). “Application of adaptive Kalman filter to identify a five story frame structure using NCREE experimental data.” Proc. Struct. Saf. Reliab., Taylor & Francis, Newport Beach, CA.
Shinozuka, M., and Ghanem, R. (1995). “Structural system identification. II. Experimental verification.” J. Eng. Mech., 265–273.
Smyth, A. W., Masri, S. F., Kosmatopoulos, E. B., Chasssiakos, A. G., and Caughey, T. K. (2002). “Development of adaptive modeling techniques for non-linear hysteretic systems.” Int. J. Non Linear Mech., 37(8), 1435–1451.
Tanaka, Y., and Sato, T. (2004). “Efficient system identification algorithm using Monte Carlo filter and its applications.” Proc. SPIE, Society of Photo-optical Instrumentation, Bellingham, WA.
Van Overschee, P., and De Moor, B. (1996). Subspace identification for linear systems, theory implementation application, Kluwer, Dordrecht, Netherlands.
Weng, J. H., Loh, C. H., and Yang, J. N. (2009). “Experimental study of damage detection by data-driven subspace identification and finite-element model updating.” J. Struct. Eng., 1533–1544.
Wu, M., and Smyth, A. W. (2007). “Application of unscented Kalman filter for real-time nonlinear structural system identification.” Struct. Control Health Monit., 14(7), 971–990.
Yang, J. N., and Huang, H. (2006). “Sequential non-linear least-square estimation for damage identification of structures.” Int. J. Non Linear Mech., 41(1), 124–140.
Yang, J. N., and Huang, H. (2007). “Sequential non-linear least-square estimation for damage identification of structures with unknown inputs and unknown outputs.” Int. J. Non Linear Mech., 42(5), 789–801.
Yang, J. N., Huang, H., and Pan, S. (2009). “Adaptive quadratic sum-square error for structural damage identification.” J. Eng. Mech., 67–77.
Yang, J. N., and Lin, S. (2004). “On-line identification of nonlinear hysteretic structures using an adaptive tracking technique.” Int. J. Non-Linear Mech., 39(9), 1481–1491.
Yang, J. N., Lin, S., Huang, H., and Zhou, L. (2006). “An adaptive extended Kalman filter for structural damage identification.” Struct. Control Health Monit., 13(4), 849–867.
Yang, J. N., Pan, S., and Huang, H. (2007a). “An adaptive extended Kalman filter for structural damage identification II, unknown inputs.” Struct. Control Health Monit., 14(3), 497–521.
Yang, J. N., Pan, S., and Lin, S. (2007b). “Least square estimation with unknown excitations for damage identification of structures.” J. Eng. Mech., 12–21.
Yoshida, I., and Sato, T. (2002). “Healthy monitoring algorithm by the Monte Carlo filter based on non-Gaussian noise.” J. National Disaster Sci., 24(2), 101–107.
Yun, C. B., Lee, H. J., and Lee, C. G. (1997). “Sequential prediction error method for structural identification.” J. Eng. Mech., 115–123.
Zhou, L., Wu, S. Y., and Yang, J. N. (2008). “Experimental study of an adaptive extended Kalman filter for structural damage identification.” J. Infrastruct. Syst., 42–51.
Information & Authors
Information
Published In
Journal of Structural Engineering
Volume 140 • Issue 3 • March 2014
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Jun 6, 2011
Accepted: Feb 25, 2013
Published online: Feb 27, 2013
Published in print: Mar 1, 2014
Discussion open until: Mar 31, 2014
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.