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首页> 外文期刊>Bulletin of earthquake engineering >Optimum weighted arithmetic means of peak- and spectral-based intensity measures for probabilistic seismic demand modeling of modularized suspended buildings
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Optimum weighted arithmetic means of peak- and spectral-based intensity measures for probabilistic seismic demand modeling of modularized suspended buildings

机译:Optimum weighted arithmetic means of peak- and spectral-based intensity measures for probabilistic seismic demand modeling of modularized suspended buildings

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摘要

Optimum intensity measures of ground motions have been extensively investigated in order to improve the quality of probabilistic seismic demand models. Common scalar-type intensity measures prevail in availability and computability but are less satisfactory when predicting seismic responses of systems with multiple contributing vibration modes and different forms of nonlinearity. For complex primary-secondary tuning structures, such as the modularized suspended building systems, optimal forms of scalar-type intensity measures have yet to be investigated. This study addresses this gap and particularly explores weighted arithmetic means of spectral accelerations or peak ground responses. Multi-objective optimizations of the weighting factors and the IM parameters are conducted for 5 performance indices and 10 targeted responses of a 10-story modularized suspended building on a shallow foundation when subjected to 660 ground motions. Frequency domain analysis and Pareto pattern analysis are used to interpret optimization results. It is shown that multiple elastic and elongated modes are attended to by the combination of spectral accelerations with various levels of modal periods and damping ratios, leading to a 0.22 proficiency index and a 0.96 correlation index which are 38% and 5% improvements from those of PGA. Optimal combinations of fractional order peak ground responses show distributions of derivative order between 0 and 3, achieving a 0.25 proficiency index and a 0.95 correlation index, but with notably fewer optimizing parameters.

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