• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1998년도 봄 학술발표회 논문집
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• pp.157-164
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• 1998

An elasto-plastic stochastic finite element method is developed to evaluate the probability of failure of the underground structure. The Mohr-Coulomb failure criteria is adopted for yield condition. The material properties such as the elastic modulus and the cohesion are assumed to be statistically independent random variables which are modeled as spatial stochastic fields. The displacements around the excavated area and the probability of the failure are examined by varying the coefficient of variance for each variables. It is found that the developed procedure can provide the proper probabilistic information about the failure of the underground structure

• Journal of applied mathematics & informatics
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• 제28권3_4호
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• pp.863-875
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• 2010

In this paper we apply the martingale approach, which has been widely used in mathematical finance, to investigate the optimal investment problem for an insurer under the criterion of mean-variance. When the risk and security assets are described by the L$\acute{e}$vy processes, the closed form solutions to the maximization problem are obtained. The mean-variance efficient strategies and frontier are also given.

• Structural Engineering and Mechanics
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• 제32권1호
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• pp.21-36
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• 2009

An importance sampling method is presented for computing the first passage probability of elasto-plastic structures under stochastic excitations. The importance sampling distribution corresponds to shifting the mean of the excitation to an 'adapted' stochastic process whose future is determined based on information only up to the present. A stochastic control approach is adopted for designing the adapted process. The optimal control law is determined by a control potential, which satisfies the Bellman's equation, a nonlinear partial differential equation on the response state-space. Numerical results for a single-degree-of freedom elasto-plastic structure shows that the proposed method leads to significant improvement in variance reduction over importance sampling using design points reported recently.

• Structural Engineering and Mechanics
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• 제49권1호
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• pp.41-64
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• 2014

The main objective of this article is the exploitation of a stochastic hybrid mesh-free method based on stochastic generalized finite difference (SGFD), Newmark finite difference (NFD) methods and Monte Carlo simulation for thermoelastic wave propagation and coupled thermoelasticity analysis based on GN theory (without energy dissipation). A thick hollow cylinder with Gaussian uncertainty in mechanical properties is considered as an analyzed domain for the problem. The effects of uncertainty in mechanical properties with various coefficients of variations on thermo-elastic wave propagation are studied in details. Also, the time histories and distribution on thickness of cylinder of maximum, mean and variance values of temperature and radial displacement are studied for various coefficients of variations (COVs).

• Journal of the Korean Data and Information Science Society
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• 제23권6호
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• pp.1309-1317
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• 2012

Stochastic weather generators are commonly used to simulate time series of daily weather, especially precipitation amount. Recently, a generalized linear model (GLM) has been proposed as a convenient approach to fitting these weather generators. In this paper, a stochastic weather generator is considered to model the time series of daily precipitation at Seoul in South Korea. As a covariate, global temperature is introduced to relate long-term temporal scale predictor to short-term temporal predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate time series of seasonal total precipitation in the GLM weather generator as covariates. It is veri ed that the addition of these covariates does not distort the performance of the weather generator in other respects.

• 대한교통학회지
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• 제9권1호
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• pp.47-56
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• 1991

With respect to stochastic delays at linked signals the solid quantitative information has not been available as yet. On the basis of field data the values of "I" (variance-mean ration of flow) were related with the rate of flow. The stochastic delays with specific "I" values were obtained from the distribution of overflow queue, which were calculated by the use of Markov chains. This examination of the results led to the derivation of a simple method for calculating stochastic dclays through the introduction of "I" into Miller's model. The good agreement was shown between the model and the field. The relationships between the cycle lengths and delays were examinated in a large number of conditions with regard to degree of saturation. signal split and link length. Within the practical range of cycle length uniform delays were dominant and no critical point was found in terms of minimum, delay. In highly saturated conditions however the weight of stochastic delay is noticeable.

• 한국농공학회지
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• 제22권3호
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• pp.75-87
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• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• Structural Engineering and Mechanics
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• 제86권3호
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• pp.349-359
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• 2023

This paper presents a technique for determining the optimal number of elements in stochastic finite element analysis based on reliability analysis. Using the change-of-variable perturbation stochastic finite element approach, the probability density function of the dynamic responses of stochastic structures is explicitly determined. This method combines the perturbation stochastic finite element method with the change-of-variable technique into a united model. To further examine the relationships between the random fields, discretization of the random field parameters, such as the variance function and the scale of fluctuation, is also performed. Accordingly, the reliability index is calculated based on the explicit probability density function of responses with Gaussian or non-Gaussian random fields in any number of elements corresponding to the random field discretization. The numerical examples illustrate the effectiveness of the proposed method for a one-dimensional cantilever reinforced concrete column and a two-dimensional steel plate shear wall. The benefit of this method is that the probability density function of responses can be obtained explicitly without the use simulation techniques. Any type of random variable with any statistical distribution can be incorporated into the calculations, regardless of the restrictions imposed by the type of statistical distribution of random variables. Consequently, this method can be utilized as a suitable guideline for the efficient implementation of stochastic finite element analysis of structures, regardless of the statistical distribution of random variables.

• Wind and Structures
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• 제27권6호
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• pp.399-416
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• 2018

In order to reveal the independent relationship between track irregularity and wind loads, the stochastic characteristics of train-bridge coupling systems subjected to wind loads were investigated by the multi-sample calculation. The vehicle was selected as 23 degrees of freedom dynamical model, and the bridge was described by three-dimensional finite element model. It was assumed that the wind loads were random processes with strong spatial correlation, while the track irregularities were stationary random ones. As a case study, a high-speed train running on a cable-stayed bridge subjected to wind loads was studied. The effect of rail irregularities was deemed to be independent of the effect of wind excitations on the coupling system in the same wind circumstance for the same project, leading to the conclusion that the effect of wind loads and moving vehicle could be calculated separately. The variance results of the stochastic responses of vehicle-bridge coupling system under the action of wind loads and rail irregularities together were equivalent to the sum of the variance of the responses induced by each excitation. Therefore, when one of the input excitations is different, only the effect of changed loads needs to be assessed. Moreover, the new calculated results were combined with the effect of unchanged loads to present the stochastic response of coupling system subjected to the different excitations, reducing the cost of computations. The stochastic characteristics, the CFD (cumulative distribution function) of the coupling system with different wind velocities, vehicle speed, and vehicle marshalling were studied likewise.

• Advances in materials Research
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• 제13권4호
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• pp.299-319
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• 2024

Due to higher strength-to-weight ratio of composite laminates, they find uses in many weight-sensitive applications like aerospace, automobile and marine structures. From a reliability point of view, accurate prediction of failure of these structures is important. Due to the complexities in the manufacturing processes of composite laminates, there is a variation in the material properties and geometric parameters. Hence stochastic aspects are important while designing the composite laminates. Many existing works of composite laminate failure analysis are based on the deterministic approach but it is important to consider the randomness in the material properties, geometry and loading to predict accurate failure loads. In this paper the statistics of the ultimate failure load of the [0/θ]_s laminated composite plate (LCP) containing the edge crack and voids subjected to the tensile loading are presented in terms of the mean and coefficient of variance (COV). The objective is to better the efficacy of laminate failure by predicting the statistics of the ultimate failure load of LCP with random material, geometric and loading parameters. The stochastic analysis is done by using the extended finite element method (XFEM) combined with the second-order perturbation technique (SOPT). The ultimate failure load of the LCP is obtained by ply-by-ply failure analysis using the ply discount method combined with the Tsai-Wu failure criterion. The aim is to know the effect of the stacking sequence, crack length, crack angle, location of voids and number of voids on the mean and corresponding COV of the ultimate failure load of LCP is investigated. The results of the ultimate failure load obtained by the present method are in good agreement with the existing experimental and numerical results. It is observed that [0/θ]_s LCPs are very sensitive to the randomness in the crack length, applied load, transverse tensile strength of the laminate and modulus of elasticity of the material, so precise control of these parameters is important. The novelty of the present study is, the stochastic implementation in XFEM for the failure prediction of LCPs containing crack and voids.