Influence of Dominant Response Modes on Structural Seismic Demand Modeling
Publication: Journal of Structural Engineering
Volume 142, Issue 1
Abstract
A seismic demand model attempts to describe the behavior of a structure in terms of a set of predictor variables that represents the loading. For buildings, the most frequently used demand parameter and predictor variables are the maximum interstory drift ratio (MIDR) and the spectral accelerations of the ground motion at various modal periods, respectively. An adequate and optimal demand model should be independent of the suite of records that is used to calibrate it. It is shown that this is not the case with currently used demand models and that the dominant dynamic modes imposed by the ground motion suite have a significant effect on the model predictions. In this study, this influence is quantified in terms of the coefficient of partial determination. It is shown that the marginal contribution of the included variables in the demand model is dependent on the response mode that yields the MIDR. An alternative method of estimating the regression coefficients via Ridge estimation is discussed as an approach that minimizes the influence of the dominant mode on the demand model. The performance of the Ridge estimation is compared with the least squares (unbiased) counterpart using the cross-validation method. These findings have a major impact on the selection of ground motions for seismic assessment of structures.
Get full access to this article
View all available purchase options and get full access to this article.
References
Ahmadi, A. (2014). “Ground motion selection and seismic demand modeling: Concerns, considerations and challenges.” Ph.D. dissertation Univ. of California, Davis, CA.
Alavi, B., and Krawinkler, H. (2004). “Behavior of moment-resisting frame structures subjected to near-fault ground motions.” Earthquake Eng. Struct. Dyn., 33(6), 687–706.
Aslani, H., and Miranda, E. (2003). “Probabilistic response assessment for building-specific loss estimation.”, Pacific Earthquake Engineering Research Center, Univ. of California at Berkeley, Berkeley, CA.
Baker, J. W., and Cornell, C. A. (2006). “Spectral shape, epsilon and record selection.” Earthquake Eng. Struct. Dyn., 35(9), 1077–1095.
Baker, J. W., and Shome, N. (2009). “Evaluation of point-of-comparison methodology.” Evaluation of ground motion selection and modification methods: predicting median interstory drift response of buildings, Pacific Earthquake Engineering Research center, Univ. of California, Berkeley, CA.
Campbell, K. W., and Bozorgnia, Y. (2012). “A comparison of ground motion prediction equations for arias intensity and cumulative absolute velocity developed using a consistent database and functional form.” Earthquake Spectra, 28(3), 931–941.
Haselton, C. B., et al. (2009). “Evaluation of ground motion selection and modification methods: Predicting median interstory drift response of buildings.”, Pacific Earthquake Engineering Research center, Univ. of California, Berkeley, CA.
Kalkan, E., and Kunnath, S. K. (2007). “Effective cyclic energy as a measure of seismic demand.” J. Earthquake Eng., 11(5), 725–751.
Kunnath, S. K., et al. (2004). “Modeling and response prediction in performance-based seismic evaluation: Case studies of instrumented steel moment-frame buildings.” Earthquake Spectra, 20(3), 883–915.
Kutner, M. H., Nachtsheim, C. J., and Neter, J. (2004). Applied linear regression models, 4th Ed., McGraw-Hill Irwin, New York.
Luco, N., et al. (2003). “Evaluation of predictors of non-linear seismic demands using ‘fishbone’ models of SMRF buildings.” Earthquake Eng. Struct. Dyn., 32(14), 2267–2288.
Luco, N., and Cornell, C. A. (2007). “Structure-specific scalar intensity measures for near-source and ordinary earthquake ground motions.” Earthquake Spectra, 23(2), 357–392.
Lukas, M. A. (2006). “Robust generalized cross-validation for choosing the regularization parameter.” Inverse Prob., 22(5), 1883–1902.
Lukas, M. A. (2010). “Robust GCV choice of the regularization parameter for correlated data.” J. Integral Equ. Appl., 22(3), 519–547.
Mackie, K., and Stojadinovic, B. (2004). “Improving probabilistic seismic demand models through refined intensity measures.” Proc., 13th World Conf. on Earthquake Engineering, Canadian Association for Earthquake Engineering (CAEE), Vancouver, BC, Canada.
Medina, R. A and Krawinkler, H. (2005). “Evaluation of drift demands for the seismic performance assessment of frames.” J. Struct. Eng., 1003–1013.
Ohtori, Y., et al. (2004). “Benchmark control problems for seismically excited nonlinear buildings.” J. Eng. Mech., 366–385.
OpenSees [Computer software]. Berkeley, CA, Univ. of California.
Shome, N., and Bazzurro, P. (2009). “Comparison of vulnerability of a new high-rise concrete moment frame structure using HAZUS and nonlinear dynamic analysis.” Proc., 10th Int. Conf. on Structural Safety and Reliability, CRC Press, Boca Raton, FL.
Shome, N., and Cornell, C. (1998). “Normalization and scaling accelerograms for nonlinear structural analysis.” Proc., 6th U.S. National Conf. on Earthquake Engineering, Earthquake Engineering Research Institute, Oakland, CA.
Shome, N., and Luco, N. (2010). “Loss estimation of multi-mode dominated structures for a scenario of earthquake event.” Proc., 9th U.S. National and 10th Canadian Conf. on Earthquake Engineering, Earthquake Engineering Research Institute, Oakland, CA.
Information & Authors
Information
Published In
Journal of Structural Engineering
Volume 142 • Issue 1 • January 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Jun 11, 2014
Accepted: May 8, 2015
Published online: Jul 3, 2015
Discussion open until: Dec 3, 2015
Published in print: Jan 1, 2016
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.