Experimental evaluation of four ground-motion scaling methods for dynamic response-history analysis of nonlinear structures
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Abstract
This paper experimentally evaluates four methods to scale earthquake ground-motions within an ensemble of records to minimize the statistical dispersion and maximize the accuracy in the dynamic peak roof drift demand and peak inter-story drift demand estimates from response-history analyses of nonlinear building structures. The scaling methods that are investigated are based on: (1) ASCE/SEI 7–10 guidelines; (2) spectral acceleration at the fundamental (first mode) period of the structure, Sa(T1); (3) maximum incremental velocity, MIV; and (4) modal pushover analysis. A total of 720 shake-table tests of four small-scale nonlinear building frame specimens with different static and dynamic characteristics are conducted. The peak displacement demands from full suites of 36 near-fault ground-motion records as well as from smaller “unbiased” and “biased” design subsets (bins) of ground-motions are included. Out of the four scaling methods, ground-motions scaled to the median MIV of the ensemble resulted in the smallest dispersion in the peak roof and inter-story drift demands. Scaling based on MIValso provided the most accurate median demands as compared with the “benchmark” demands for structures with greater nonlinearity; however, this accuracy was reduced for structures exhibiting reduced nonlinearity. The modal pushover-based scaling (MPS) procedure was the only method to conservatively overestimate the median drift demands.
Publication type | Article |
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Publication Subtype | Journal Article |
Title | Experimental evaluation of four ground-motion scaling methods for dynamic response-history analysis of nonlinear structures |
Series title | Bulletin of Earthquake Engineering |
DOI | 10.1007/s10518-016-0052-z |
Volume | 15 |
Issue | 5 |
Year Published | 2017 |
Language | English |
Publisher | Springer |
Contributing office(s) | Earthquake Science Center |
Description | 26 p. |
First page | 1899 |
Last page | 1924 |
Google Analytic Metrics | Metrics page |