Kriging metamodeling for approximation of high-dimensional wave and surge responses in real-time storm/hurricane risk assessment

Jia, Gaofeng; Taflanidis, Alexandros A.

doi:10.1016/j.cma.2013.03.012

Cited by 155 publications

(93 citation statements)

References 41 publications

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“…As soon as the kriging metamodel is established based on this database, it can be then used to efficiently predict the responses for any other θ desired. Calculation of b s m i x ð Þ and σ 2 i x ð Þ can be also vectorized, 36 something that will be leveraged in the numerical optimization discussed in the next section.…”

Section: Problem Formulationmentioning

confidence: 99%

Modification of stochastic ground motion models for matching target intensity measures

Tsioulou

Taflanidis

Galasso

2017

Earthq Engng Struct Dyn

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SummaryStochastic ground motion models produce synthetic time-histories by modulating a white noise sequence through functions that address spectral and temporal properties of the excitation. The resultant ground motions can be then used in simulation-based seismic risk assessment applications. This is established by relating the parameters of the aforementioned functions to earthquake and site characteristics through predictive relationships. An important concern related to the use of these models is the fact that through current approaches in selecting these predictive relationships, compatibility to the seismic hazard is not guaranteed. This work offers a computationally efficient framework for the modification of stochastic ground motion models to match target intensity measures (IMs) for a specific site and structure of interest. This is set as an optimization problem with a dual objective. The first objective minimizes the discrepancy between the target IMs and the predictions established through the stochastic ground motion model for a chosen earthquake scenario. The second objective constraints the deviation from the model characteristics suggested by existing predictive relationships, guaranteeing that the resultant ground motions not only match the target IMs but are also compatible with regional trends. A framework leveraging kriging surrogate modeling is formulated for performing the resultant multi-objective optimization, and different computational aspects related to this optimization are discussed in detail. The illustrative implementation shows that the proposed framework can provide ground motions with high compatibility to target IMs with small only deviation from existing predictive relationships and discusses approaches for selecting a final compromise between these two competing objectives. | INTRODUCTIONThe growing interest in performance-based earthquake engineering 1,2 and in simulation-based, seismic risk assessment approaches 3,4 has increased in the past decades the relevance of ground motion modeling techniques. These techniques describe the entire time-series of seismic excitations, providing a characterization appropriate for dynamic time-history analysis. UndoubtedlyThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Problem Formulationmentioning

confidence: 99%

Modification of stochastic ground motion models for matching target intensity measures

Tsioulou

Taflanidis

Galasso

2017

Earthq Engng Struct Dyn

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“…The performance of the metamodel can be improved primarily by increasing the number of support points n m or by their proper selection (Picheny et al 2010). Other potential strategies for such performance improvement could be the change of the correlation function or the basis functions (Jia and Taflanidis 2013).…”

Section: Validation Of Metamodelmentioning

confidence: 98%

“…A specific consequence measure utilized in a variety of different risk applications, for example, within system reliability analysis or life-cycle cost estimation (Ellingwood 2001;Goulet et al 2007;Jia and Taflanidis 2013), is the probability that some response quantity z k (e.g., peak interstory drift for a structure) will exceed some thresholdˇk that determines acceptable performance. For certain applications, for example, within seismic risk assessment, where this concept can be used to represent the fragility of system components, it is common to incorporate a prediction error in this definition (Porter et al 2006;Taflanidis et al 2013b); this can be equivalently considered as the aforementioned threshold corresponding to an uncertain quantity with some chosen distribution (this distribution ultimately determines the cumulative distribution function for the component fragilities).…”

Section: Introductionmentioning

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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“…Detailed introductions to the Kriging interpolation procedure can be found in many existing publications [35][36][37][38]. In the Kriging system, the response Y(x) is expressed as the following regression model: x ,x ,...,x n = x with x R p i ∈ , P is the number of the design variables.…”

Section: Kriging Interpolationmentioning

confidence: 99%

“…However, this approach is quite complicated in its mathematical form. Kriging is a response surface method for spatial data interpolation and is widely used in the area of geology and aerology research [35][36][37][38]. Kriging uses spatial relationships of known points and their distribution to predict an unknown point, and it is a statistical, unbiased, and minimum variance predictor in which errors can be determined at specified points.…”

Section: The Rom Developmentmentioning

confidence: 99%

Evaluation of the potential of retrofitting a coal power plant to oxy-firing using CFD and process co-simulation

Fei

Black

Szuhánszki

et al. 2015

Fuel Processing Technology

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Abstract:In this paper, a new approach has been developed for estimating the potential of retrofitting an existing power plant to oxy-firing and determining the safe operation regime of the boiler under enriched oxy-combustion. A Computational Fluid Dynamics (CFD) technique has been employed to simulate the complex coal combustion and heat transfer to the furnace water walls and heat exchangers under air-firing and oxy-firing conditions. A set of reduced order models (ROMs) have been developed to link the CFD predictions to the efficient whole plant process models in order to simulate the performance of the power plant under different load and oxygen enrichment conditions if retrofitted to oxy-firing. The simulations of a 500MW e power plant unit indicate that it is possible to retrofit it to oxy-firing without affecting the overall performance of the unit. Similar heat transfer characteristics and steam generation can be achieved to those under air-firing, although the temperature of the boiler wall may increase after retrofitting. The upper limits for the oxygen enrichment at different power loads are identified and the feasible oxygen concentration range within 25%-27% is suggested for the oxy-coal operation of this retrofitted power plant investigated.However, the peak temperature on the superheater platen 2 may increase in the oxy-coal mode at high power load beyond 450MW e . If the tube material cannot endure the temperature increase, then an upgrade on the material is required.

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Kriging metamodeling for approximation of high-dimensional wave and surge responses in real-time storm/hurricane risk assessment

Cited by 155 publications

References 41 publications

Modification of stochastic ground motion models for matching target intensity measures

Modification of stochastic ground motion models for matching target intensity measures

Natural Hazard Probabilistic Risk Assessment Through Surrogate Modeling

Evaluation of the potential of retrofitting a coal power plant to oxy-firing using CFD and process co-simulation

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