• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.9 s.252
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• pp.1116-1123
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• 2006

In this study, the effective method for reliability estimation is proposed using tow-staged kriging metamodel and genetic algorithm. Kriging metamodel can be determined by appropriate sampling range and the number of sampling points. The first kriging metamodel is made based on the proposed sampling points. The advanced f'=rst order reliability method is applied to the first kriging metamodel to determine the reliability and most probable failure point(MPFP) approximately. Then, the second kriging metamodel is constructed using additional sampling points near the MPFP. These points are selected using genetic algorithm that have the maximum mean squared error. The Monte-Carlo simulation is applied to the second kriging metamodel to estimate the reliability. The proposed method is applied to numerical examples and the results are almost equal to the reference reliability.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2001.10a
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• pp.187-190
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• 2001

Response surface method is applied to evaluate the reliability of laminated composite panels. Since the linear and nonlinear first-ply failure load are computed using deterministic finite element analysis, new probabilistic finite element analysis is not necessary. Tsai-Wu criterion is used to construct the limit state suface. Material properties, layer thickness and lamina strengths of laminated composite panel are treated as random design variables. feasibility and accuracy of current method is validated using Monte-Carlo method Which perform thousand times of finite element analysis directly.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.6
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• pp.1-10
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• 2013

Probabilistic nonlinear first ply failure loads of flat composite panels and nonlinear buckling loads of curved composite panels with cutouts are estimated to provide the more reliable main load carrying structure in the renewable energy industry and offshore structures. The response surface method approximates limit state surface to a second order polynomial form of random variables with the results of deterministic finite element analyses at given sampling design points. Furthermore, the iterative linear interpolation scheme is used to obtain a more accurate approximation of the limit state surface near the most probable failure point (MPFP). The advanced first order second moment method and the Monte Carlo method are performed on an approximated limit state surface to evaluate the probability of failure. Finally, the sensitivity of the reliability index with respect to transformed random variables is investigated to figure out the main random variables that have an effect on failures.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.14 no.2
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• pp.213-224
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• 2001

확률론적 구조설계 최적화는 구조물의 역학적 특성이나 하중의 불확실성이나 임의성과 같은 변동성을 정량적이고 합리적으로 고려할 수 있다는 점에서 기존의 전통적인 확정론적 최적화와 비교된다. 확률론적 최적화의 방법론으로는 개선된 일계이차모멘트법을 이용하는 신뢰도지수에 기반한 접근법(MPFP search)이 널리 알려져 있으며, 최근 목표성능치에 기반한 접근법(MPTP search)이 새롭게 제안되었다. 본 논문에서는 이들 두 가지 접근법에 대한 정식화를 수행하고, 특히 탐색과정에서 소모적인 반복계산을 발견하고 제거하는 알고리즘을 제시하였다. 예제에서 두 접근법에 의한 확률론적 최적화를 수행하고 구조설계 최적화의 관점에서 두 접근법의 장단점을 비교·검토하였다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.10a
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• pp.32-39
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• 2000

Probabilistic structural design optimization, which is characterized by the so-called probabilistic. constraints which introduce permissible probability of violation, is preferred to deterministic design optimization since unpredictable inherent uncertainties and randomness in structural and environmental properties are to be taken quantitatively into account by probabilistic design optimization. In this paper, the well-known reliability index based MPFP(Most Probable Failure Point) search approach and the newly introduced target performance measure based MPTP(Minimum Performance Target Point) search approach are summarized and compared. The present comparison focuses on the number of iterations required for the estimation of probabilistic constraints and a technique for improvement which removes exhaustive iterations is presented as well. A 10 bar truss problem is examined for this.

• Journal of the Society of Naval Architects of Korea
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• v.33 no.1
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• pp.98-108
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• 1996

In the reliability analysis of general structures, the limit state equations are implicit and cannot be described in closed form. Thus, sampling methods such as the Crude Monte-Carlo simulation, and probabilistic FEM are often used, but these methods are not so effective in view of computational cost, because a number of structural analysis are required and the derivatives must be calculated for probabilistic FEM. Alternatively the response surface approach, which approximates the limit state surface by using several results of structural analysis in the region adjacent to MPFP, could be applied effectively. In this paper, the central composite design, Bucher-Bourgund method and the approximation method using artificial neural network are studied for the calculation of probability of failure by the response surface method. Through the example comparisons, it is found that Bucher-Bourgund method is very effective and Neural network method for the reliability analysis is comparable with other methods. Specially, the central composite design method is found to be rational and useful in terms of mathematical consistency and accuracy.