Addressing the different sources of excitation variability in seismic response distribution estimation using kriging metamodeling

Kyprioti, Aikaterini P.; Taflanidis, Alexandros A.

doi:10.1002/eqe.3696

Cited by 1 publication

(27 citation statements)

References 56 publications

(209 reference statements)

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“…Of course, it should be noted that the proposed method is heuristic in a way that it assumes that the mean estimate obtained from the crude homoscedastic nugget assumption, m(𝐱), gives a reasonably reliable estimate. This assumption is justified by past studies, 22,32 that have shown that for seismic applications the mean-field prediction is relatively robust to the choice of the variance model, especially compared to the level of variability observed in the sample replications. This was also observed in more general, non-seismic, applications investigated in ref.…”

Section: Algorithmic Implementation Of the Improved Stochastic Emulationmentioning

confidence: 98%

“…The illustrative example revisits the examples discussed in refs. [22,32] to allow a direct comparison between the original (PR-SE) and updated (ER-SE) stochastic emulation algorithms across different settings for quantifying seismic hazard and the underlying aleatoric variability. The example considers the approximation of the distribution for peak drift and absolute floor acceleration EDPs for a three-story concrete moment resisting frame (MRF), with seismic excitation described through different stochastic ground motion models.…”

Section: Illustrative Examplementioning

confidence: 99%

“…The assumption of lognormal EDP distribution under the assumed (in each case) aleatoric variability has been shown to provide high statistical accuracy for all three options in previous studies. 22,32 The first ground motion model, denoted GM 1 corresponds to a point source stochastic model, 47 with deterministic predictive relationships. The ground motion model characteristics identical to the ones adopted in study.…”

Section: Excitation Models and Aleatoric Uncertainty Descriptionmentioning

confidence: 99%

“…Details for estimation of the integral in Equation ( 6), including establishing the Monte Carlo approximation for 𝑝(𝐳|𝐱 𝑠 , 𝐱 𝑚 ) are included in ref. [22]. The estimation of p(ln(𝑧)|𝐱 𝑣 ) based on the stochastic emulation predictions is discussed in Appendix B.…”

Section: Implementation Details and Validation Metricsmentioning

confidence: 99%

“…20 This variability is large and crucial to the downstream analysis of structural performance estimation 1 necessitating for a high-precision estimation of the underlying EDP distribution. Meanwhile, the level of variability depends significantly on structural and seismic explanatory inputs 22 (representing a heteroscedastic behavior) increasing the complexity of the risk assessment.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic emulation with enhanced partial‐ and no‐replication strategies for seismic response distribution estimation

Yi,

Taflanidis

2024

Earthq Engng Struct Dyn

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Modern performance earthquake engineering practices frequently require a large number of time‐consuming non‐linear time‐history simulations to appropriately address excitation and structural uncertainties when estimating engineering demand parameter (EDP) distributions. Surrogate modeling techniques have emerged as an attractive tool for alleviating such high computational burden in similar engineering problems. A key challenge for the application of surrogate models in earthquake engineering context relates to the aleatoric variability associated with the seismic hazard. This variability is typically expressed as high‐dimensional or non‐parametric uncertainty, and so cannot be easily incorporated within standard surrogate modeling frameworks. Rather, a surrogate modeling approach that can directly approximate the full distribution of the response output is warranted for this application. This approach needs to additionally address the fact that the response variability may change as input parameter changes, yielding a heteroscedastic behavior. Stochastic emulation techniques have emerged as a viable solution to accurately capture aleatoric uncertainties in similar contexts, and recent work by the second author has established a framework to accommodate this for earthquake engineering applications, using Gaussian Process (GP) regression to predict the EDP response distribution. The established formulation requires for a portion of the training samples the replication of simulations for different descriptions of the aleatoric uncertainty. In particular, the replicated samples are used to build a secondary GP model to predict the heteroscedastic characteristics, and these predictions are then used to formulate the primary GP that produces the full EDP distribution. This practice, however, has two downsides: it always requires minimum replications when training the secondary GP, and the information from the non‐replicated samples is utilized only for the primary GP. This research adopts an alternative stochastic GP formulation that can address both limitations. To this end, the secondary GP is trained by measuring the square of sample deviations from the mean instead of the crude sample variances. To establish the primitive mean estimates, another auxiliary GP is introduced. This way, information from all replicated and non‐replicated samples is fully leveraged for estimating both the EDP distribution and the underlying heteroscedastic behavior, while formulation accommodates an implementation using no replications. The case study examples using three different stochastic ground motion models demonstrate that the proposed approach can address both aforementioned challenges.

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Section: Algorithmic Implementation Of the Improved Stochastic Emulationmentioning

confidence: 98%

Section: Illustrative Examplementioning

confidence: 99%