On the effect of near-field excitations on the reliability-based performance and design of base-isolated structures

Jensen, Héctor A.; Kusanovic, Danilo S.

doi:10.1016/j.probengmech.2014.03.003

Cited by 25 publications

(9 citation statements)

References 27 publications

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“…The relevance of techniques that model acceleration time-series of seismic events has increased during the past decades due to the growing popularity of simulation-based probabilistic seismic risk assessment [1][2][3] and performance-based earthquake engineering. [4][5][6] Though the most popular methodology for performing this task is the selection of real (ie, recorded from past events) ground motions, [7][8][9][10] potentially scaled based on a target intensity measure (IM), an alternative philosophy is the use of simulated ground motions.…”

Section: Introductionmentioning

confidence: 99%

Hazard‐compatible modification of stochastic ground motion models

Tsioulou

Taflanidis

Galasso

2018

Earthq Engng Struct Dyn

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Summary A computationally efficient framework is presented for modification of stochastic ground motion models to establish compatibility with the seismic hazard for specific seismicity scenarios and a given structure/site. The modification pertains to the probabilistic predictive models that relate the parameters of the ground motion model to seismicity/site characteristics. These predictive models are defined through a mean prediction and an associated variance, and both these properties are modified in the proposed framework. For a given seismicity scenario, defined for example by the moment magnitude and source‐to‐site distance, the conditional hazard is described through the mean and the dispersion of some structure‐specific intensity measure(s). Therefore, for both the predictive models and the seismic hazard, a probabilistic description is considered, extending previous work of the authors that had examined description only through mean value characteristics. The proposed modification is defined as a bi‐objective optimization. The first objective corresponds to comparison for a chosen seismicity scenario between the target hazard and the predictions established through the stochastic ground motion model. The second objective corresponds to comparison of the modified predictive relationships to the pre‐existing ones that were developed considering regional data, and guarantees that the resultant ground motions will have features compatible with observed trends. The relative entropy is adopted to quantify both objectives, and a computational framework relying on kriging surrogate modeling is established for an efficient optimization. Computational discussions focus on the estimation of the various statistics of the stochastic ground motion model output needed for the entropy calculation.

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Section: Introductionmentioning

confidence: 99%

Hazard‐compatible modification of stochastic ground motion models

Tsioulou

Taflanidis

Galasso

2018

Earthq Engng Struct Dyn

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“…Stochastic ground motion models (Boore 2003;Rezaeian and Der Kiureghian 2010;Vetter et al 2016) have been gaining increased attention within the structural engineering community for description of seismic hazard (Jensen and Kusanovic 2014;Gidaris et al 2014). They are based on modulation of a stochastic sequence (typically white noise), w 2 W, through functions that address the frequency and time-domain characteristics of the excitation.…”

Section: Seismic Risk Modeling Through Stochastic Ground Motion Modelsmentioning

confidence: 99%

Natural Hazard Probabilistic Risk Assessment Through Surrogate Modeling

Taflanidis

Jia

Gidaris

2016

Multi-Hazard Approaches to Civil Infrastructure Engineering

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Assessment of risk under natural hazards is associated with a significant computational burden when comprehensive numerical models and simulation-based methodologies are involved. Despite recent advances in computer and computational science that have contributed in reducing this burden and have undoubtedly increased the popularity of simulation-based frameworks for quantifying/estimating risk in such settings, in many instances, such as for real-time risk estimation, this burden is still considered as prohibitive. This chapter discusses the use of kriging surrogate modeling for addressing this challenge. Kriging establishes a computationally inexpensive input/output relationship based on a database of observations obtained through the initial (expensive) simulation model. The upfront cost for obtaining this database is of course high, but once the surrogate model is established, all future evaluations require small computational effort. For illustration, two different applications are considered, involving two different hazards: seismic risk assessment utilizing stochastic ground motion modeling and real-time hurricane risk estimation. Various implementation issues are discussed, such as (a) advantages of kriging over other surrogate models, (b) approaches for obtaining high efficiency when the output under consideration is high dimensional through integration of principal component analysis, and (c) the incorporation of the prediction error associated with the metamodel into the risk assessment.

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“…Validation calculations have shown that the analytical model is able to accurately simulate the test results for both bidirectional and unidirectional loading [39]. For a detailed description and implementation of the analytical model that describe the nonlinear behavior of the isolators the reader is referred to [40,39]. A typical displacementrestoring force curve of the rubber bearing is illustrated in Fig.…”

Section: Implementation Aspectsmentioning

confidence: 99%

“…The resulting spectrum is multiplied by a ground motion spectrum after which discrete inverse Fourier transform is applied to transform the sequence back to the time domain to yield the desired ground acceleration time history. Details of the procedure as well as the characterization of the envelope function and the ground motion spectrum can be found in [43,41,42,40,44]. The duration of the excitation is taken equal to T = 30 s with a sampling interval equal to ∆T = 0.01 s. Thus, the white noise sequence contains n T = 3, 001 random variables and therefore the vector of uncertain parameters w involved in the problem is high-dimensional.…”

Section: Stochastic Excitationmentioning

confidence: 99%

Reliability sensitivity analysis of stochastic finite element models

Jensen

Mayorga

Papadimitriou

2015

Computer Methods in Applied Mechanics and Engineering

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This contribution presents a scheme for integrating a model reduction technique into a simulation-based method for reliability sensitivity analysis of a class of medium/large nonlinear finite element models under stochastic excitation. The solution of this type of problems requires a large number of finite element model re-analyses to be performed over the space of system parameters. A component mode synthesis technique is implemented to carry out the sensitivity analysis in a reduced space of generalized coordinates. The reliability sensitivity analysis is performed by an approach based on a simple post-processing of an advanced sampling-based reliability analysis. The feasibility and effectiveness of the proposed scheme is demonstrated on a bridge finite element model under stochastic ground excitation.

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On the effect of near-field excitations on the reliability-based performance and design of base-isolated structures

Cited by 25 publications

References 27 publications

Hazard‐compatible modification of stochastic ground motion models

Hazard‐compatible modification of stochastic ground motion models

Natural Hazard Probabilistic Risk Assessment Through Surrogate Modeling

Reliability sensitivity analysis of stochastic finite element models

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