No abstract

In the field of earthquake engineering, ground-motion prediction models are frequently used to estimate the peak ground acceleration (PGA) and the pseudospectral acceleration (PSA). In regions of the world where ground-motion recordings are plentiful, such as western North America (WNA), the ground-motion prediction equations are obtained using empirical methods. In other regions, such as eastern North America (ENA), with insufficient ground-motion data, alternative methods must be used to develop ground-motion prediction equations (GMPEs). The hybrid empirical method is one such method used to develop ground-motion prediction equations in areas with sparse ground motions. This method employs the stochastic simulation method to adjust empirical GMPEs developed for a region with abundant strongmotion recordings in order to estimate strong-motion parameters in a region with a sparse database. The adjustments take into account differences in the earthquake source, wave propagation, and site-response characteristics between the two regions.In this study, a hybrid empirical method is used to develop a new GMPE for ENA, using five new ground-motion prediction models developed by the Pacific Earthquake Engineering Research Center (PEER) for WNA. A new ENA GMPE is derived for a magnitude range of 5 to 8 and closest distances to the fault rupture up to 1000 km. Ground-motion prediction equations are developed for the response spectra (pseudoacceleration, 5% damped) and the PGA for hard-rock sites in ENA. The resulting ground-motion prediction model developed in this study is compared with two ENA ground-motion models used in the 2008 national seismic hazard maps as well as with available observed data for ENA.

An alternative approach based on a hybrid-empirical model is utilized to predict the ground motion relationship for eastern North America (ENA). In this approach, a stochastic model is first used to derive modification factors from the ground motions in western North America (WNA) to the ground motions in ENA. The ground motion parameters are then estimated to develop an empirical attenuation relationship for ENA using empirical ground motion relationships from WNA. We develop an empiricalstochastic source model for both regions to obtain ground motions at different magnitudedistance range of interest. At short distances ( 30 R ≤ km) and large magnitudes ( 4 . 6 ≥ w M), an equivalent point-source model is carried out to consider the effect of finite-fault modeling on the ground-motion parameters. Source focal depth and Brune stress drop are assumed to be magnitude dependent. We choose three well-defined empirical attenuation relationships for WNA in order to compare the empirical ground motion processes between the two regions. A composite functional attenuation form is defined and in turn a nonlinear regression analysis is performed using a genetic algorithm (GA) for a wide range of magnitudes and distances to develop an empirical attenuation relationship from the stochastic ground-motion estimates in ENA. The empiricalstochastic attenuation relationship for horizontal PGA and spectral acceleration (SA) are applicable to earthquakes of w M 5.0 to 8.2 at distances of up to 1000 km. The resulting attenuation model developed in this study is compared with those used in the 2002 national seismic hazard maps, derived in the 2003 EPRI studies and recorded in ENA.The comparison of the results to the other attenuation functions and the available ENA data show a reasonable agreement for the ENA ground motions.

This paper presents a new approach to selection of a set of recorded earthquake ground motions that in combination match a given site-specific design spectrum with minimum alteration. The scaling factors applied to selected ground motions are scalar values within the range specified by the user. As a result, the phase and shape of the response spectra of earthquake ground motions are not tampered with. Contrary to the prevailing scaling methods where a preset number of earthquake records (usually between a single component to seven pairs) are selected first and scaled to match the design spectrum next, the proposed method is capable of searching a set consisting of thousands of earthquake records and recommending a desired subset of records that match the target design spectrum. This task is achieved by using a genetic algorithm (GA), which treats the union of 7 records and corresponding scaling factors as a single ''individual.'' The first generation of individuals may include a population of, for example, 200 records. Then, through processes that mimic mating, natural selection, and mutation, new generations of individuals are produced and the process continues until an optimum individual (seven pairs and scaling factors) is obtained. The procedure is fast and reliable and results in records that match the target spectrum with minimal tampering and the least mean square of deviation from the target spectrum.

In this paper we present a genetic algorithm (GA)-based optimization procedure for the design of 2D, geometrical, nonlinear steel-framed structures. The approach presented uses GAs as a tool to achieve discrete nonlinear optimal or near-optimal designs. Frames are designed in accordance with the requirements of the AISC-LRFD specification. In this paper, we employ a group selection mechanism, discuss an improved adapting crossover operator, and provide recommendations on the penalty function selection. We compare the differences between optimized designs obtained by linear and geometrically nonlinear analyses. Through two examples, we will illustrate that the optimal designs are not affected significantly by the P-⌬ effects. However, in some cases we may achieve a better design by performing nonlinear analysis instead of linear analysis.