확률모델 불확실성을 고려한 구조물의 신뢰도 기반 최적설계 - 제1편: 설계 방법 (Reliability-based Structural Design Optimization Considering Probability Model Uncertainties - Part 1: Design Method)

서지정보

· 발행기관 : 한국안전학회
· 수록지 정보 : 한국안전학회지 / 27권 / 5호 / 148 ~ 157페이지
· 저자명 : 옥승용, 박원석

초록

Reliability-based design optimization (RBDO) problem is usually formulated as an optimization problem to minimize an objective function subjected to probabilistic constraint functions which may include deterministic design variables as well as random variables. The challenging task is that, because the probability models of the random variables are often assumed based on limited data, there exists a possibility of selecting inappropriate distribution models and/or model parameters for the random variables, which can often lead to disastrous consequences. In order to select the most appropriate distribution model from the limited observation data as well as model parameters, this study takes into account a set of possible candidate models for the random variables. The suitability of each model is then investigated by employing performance and risk functions. In this regard, this study enables structural design optimization and fitness assessment of the distribution models of the random variables at the same time. As the first paper of a two-part series, this paper describes a new design method considering probability model uncertainties. The robust performance of the proposed method is presented in Part 2. To demonstrate the effectiveness of the proposed method, an example of ten-bar truss structure is considered. The numerical results show that the proposed method can provide the optimal design variables while guaranteeing the most desirable distribution models for the random variables even in case the limited data are only available

영어초록

Reliability-based design optimization (RBDO) problem is usually formulated as an optimization problem to minimize an objective function subjected to probabilistic constraint functions which may include deterministic design variables as well as random variables. The challenging task is that, because the probability models of the random variables are often assumed based on limited data, there exists a possibility of selecting inappropriate distribution models and/or model parameters for the random variables, which can often lead to disastrous consequences. In order to select the most appropriate distribution model from the limited observation data as well as model parameters, this study takes into account a set of possible candidate models for the random variables. The suitability of each model is then investigated by employing performance and risk functions. In this regard, this study enables structural design optimization and fitness assessment of the distribution models of the random variables at the same time. As the first paper of a two-part series, this paper describes a new design method considering probability model uncertainties. The robust performance of the proposed method is presented in Part 2. To demonstrate the effectiveness of the proposed method, an example of ten-bar truss structure is considered. The numerical results show that the proposed method can provide the optimal design variables while guaranteeing the most desirable distribution models for the random variables even in case the limited data are only available

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