• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2529-2539
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• 2022

Stochastic and an empirical Green's function (EGF) methods are preliminarily applied to simulate strong ground motions (SGMs) at seismic stations within nuclear power plant (NPP) sites in South Korea by an assumed large earthquake with M_W6.5 (scenario earthquake) on the causative fault of the 2016 Gyeongju earthquake with M_W5.5 (mainshock). In the stochastic method, a ratio of spectral amplitudes of observed and simulated waveforms for the mainshock is assumed to be an adjustment factor. In the EGF method, SGMs by the mainshock are simulated assuming SGMs by the 2016 Gyeongju earthquake with M_W5.0 (foreshock) as the EGF. To simulate SGMs by the scenario earthquake, a ratio of fault length to width is assumed to be 2:1 in the stochastic method, and SGMs by the mainshock are assumed to be EGF in the EGF method. The results are similar based on a bias of the simulated response spectra by the two methods, and the simulated response spectra by the two methods exceeded commonly standard design response spectra anchored at 0.3 g of NPP sites slightly at a frequency band above 4 Hz, but considerable attention to interpretation is required since it is an indirect comparison.

• Structural Engineering and Mechanics
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• v.32 no.1
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• pp.1-20
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• 2009

The present contribution addresses uncertainty quantification and uncertainty propagation in structural mechanics using stochastic analysis. Presently available procedures to describe uncertainties in load and resistance within a suitable mathematical framework are shortly addressed. Monte Carlo methods are proposed for studying the variability in the structural properties and for their propagation to the response. The general applicability and versatility of Monte Carlo Simulation is demonstrated in the context with computational models that have been developed for deterministic structural analysis. After discussing Direct Monte Carlo Simulation for the assessment of the response variability, some recently developed advanced Monte Carlo methods applied for reliability assessment are described, such as Importance Sampling for linear uncertain structures subjected to Gaussian loading, Line Sampling in linear dynamics and Subset simulation. The numerical example demonstrates the applicability of Line Sampling to general linear uncertain FE systems under Gaussian distributed excitation.

• Geomechanics and Engineering
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• v.3 no.2
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• pp.153-167
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• 2011

Traditional design methods of bearing capacity of shallow foundations are deterministic in the sense that they do not explicitly consider the inherent uncertainty associated with the factors affecting bearing capacity. To account for such uncertainty, available deterministic methods rather employ a fixed global factor of safety that may lead to inappropriate bearing capacity predictions. An alternative stochastic approach is essential to provide a more rational estimation of bearing capacity. In this paper, the likely distribution of predicted bearing capacity of strip footings subjected to vertical loads is obtained using a stochastic approach based on the Monte Carlo simulation. The approach accounts for the uncertainty associated with the soil shear strength parameters: cohesion, c, and friction angle, ${\phi}$, and the cross correlation between c and ${\phi}$. A set of stochastic design charts that assure target reliability levels of 90% and 95%, are developed for routine use by practitioners. The charts negate the need for a factor of safety and provide a more reliable indication of what the actual bearing capacity might be.

• Steel and Composite Structures
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• v.8 no.2
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• pp.129-144
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• 2008

This paper demonstrates an application of the perturbation based stochastic finite element method (SFEM) for predicting the performance of structural systems made of composite sections with random material properties. The composite member consists of materials in contact each of which can surround a finite number of inclusions. The perturbation based stochastic finite element analysis can provide probabilistic behavior of a structure, only the first two moments of random variables need to be known, and should therefore be suitable as an alternative to Monte Carlo simulation (MCS) for realizing structural analysis. A summary of stiffness matrix formulation of composite systems and perturbation based stochastic finite element dynamic analysis formulation of structural systems made of composite sections is given. Two numerical examples are presented to illustrate the method. During stochastic analysis, displacements and sectional forces of composite systems are obtained from perturbation and Monte Carlo methods by changing elastic modulus as random variable. The results imply that perturbation based SFEM method gives close results to MCS method and it can be used instead of MCS method, especially, if computational cost is taken into consideration.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1993.04a
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• pp.117-124
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• 1993

The Neumann Expansion method has been used for evaluating the response variability of three dimensional truss structure resulting from the spatial variability of material properties with the aid of the finite element method, and in conjunction with the direct Monte Carlo simulation methods. The spatial variabilites are modeled as three-dimensional stochastic field. Yamazaki 〔1〕 has extended the Neumann Expansion method to the plane-strain problem to obtain the response variability of 2 dimensional stochastic systems. This paper presents the extension of the Neumann Expansion method to 3 dimensional stochastic systems. The results by the NEM are compared with those by the deterministic finite element analysis and by the direct Monte Carlo simulation method

• The Transactions of the Korean Institute of Electrical Engineers
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• v.33 no.9
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• pp.372-376
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• 1984

This paper discusses on the stochastic adaprive control which uses a least squares method in the parameter adaptation law. Especially we study on the reason why existing methods have adopted modified least squares methods. After examining the performances of these methods for time-varying systems, we propose a new method to deal with such a situation, study on the stability problem, and finally show the effectiveness of the method with a computer simulation example.

• The Pure and Applied Mathematics
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• v.22 no.3
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• pp.299-313
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• 2015

In this paper, we show the effectiveness of copulas by comparing the correlation of market data of year 2010 with those of years 2006-2009 and investigate copula functions as pricing methods of digital and rainbow options through real market data. We propose an accurate method of pricing rainbow options by using the correlation coefficients obtained from the copula functions depending on strike prices between assetes instead of simple traditional correlation coefficients.

• Communications for Statistical Applications and Methods
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• v.23 no.1
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• pp.21-32
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• 2016

We study asymptotic expansion formulae for numerical computation of Greeks (i.e. sensitivity) in finance. Our approach is based on the integration-by-parts formula of the Malliavin calculus. We propose asymptotic expansion of Greeks for a stochastic volatility model using the Greeks formula of the Black-Scholes model. A singular perturbation method is applied to derive asymptotic Greeks formulae. We also provide numerical simulation of our method and compare it to the Monte Carlo finite difference approach.

• Journal of Korean Institute of Industrial Engineers
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• v.13 no.1
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• pp.61-67
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• 1987

A tool for optimizing simulated discrete variable stochastic systems, SIMICOM was developed and presented in [5]. In this paper an evaluation of its performance and results of comparisons with other popular methods for dealing with simulation-optimization problems will be provided. Based on several test problems it is concluded that SIMICOM dominates those methods.

• Structural Engineering and Mechanics
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• v.6 no.5
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• pp.473-484
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• 1998

The first and second moments of response variables for SDOF systems with hysteretic nonlinearity are obtained by a direct linearization procedure. This adaptation in the implementation of well-known statistical linearization methods, provides concise, model-independent linearization coefficients that are well-suited for numerical solution. The method may be applied to systems which incorporate any hysteresis model governed by a differential constitutive equation, and may be used for zero or non-zero mean random vibration. The implementation eliminates the effort of analytically deriving specific linearization coefficients for new hysteresis models. In doing so, the procedure of stochastic analysis is made independent from the task of physical modeling of hysteretic systems. In this study, systems with three different hysteresis models are analyzed under various zero and non-zero mean Gaussian White noise inputs. Results are shown to be in agreement with previous linearization studies and Monte Carlo Simulation.