A data-driven approach to non-parametric reliability-based design optimization of structures with uncertain load

Kanno, Yoshihiro

doi:10.1007/s00158-019-02199-6

Cited by 17 publications

(18 citation statements)

References 53 publications

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“…The method presented in this paper reduces this gap, with retaining the guarantee of conservativeness. In other words, the method in the present paper tightens the approximation in Kanno (2019).…”

Section: Introductionmentioning

confidence: 73%

“…In this section, we formulate the design optimization problem dealt with in this paper, and briefly recall the methodology proposed in Kanno (2019).…”

Section: Problem Setting and Overview Of Data-driven Reliability-basementioning

confidence: 99%

“…Since considering uncertainty in the input distribution makes the problem structure much more elaborate, the solution methods dealing with such a problem setting have not yet been matured compared with RBDO methods assuming a deterministic probability model. As one option, a data-driven RBDO approach was proposed in Kanno (2019). This approach does not make any assumption on the distribution type and the statistical parameters, and hence is free from errors and uncertainty stemming from the conventional empirical probabilistic modeling.…”

Section: Introductionmentioning

confidence: 99%

“…The probability that the structural reliability is no smaller than the target value is called the confidence level (Noh et al, 2011;Moon et al, 2017Moon et al, , 2018Ito et al, 2018). Kanno (2019) presented a sufficient condition guaranteeing that the confidence level is no smaller than the specified value. It is worth noting that the sufficiency means conservativeness, i.e., any solution satisfying this sufficient condition satisfies the original constraint on the specified confidence level.…”

Section: Introductionmentioning

confidence: 99%

“…In section 2, we present the problems that we consider in this paper: RBDO problems of trusses and continua under the compliance constraint with a random external load. We also overview the data-driven RBDO approach in Kanno (2019). Section 3 describes how to incorporate PCA into this approach.…”

Section: Introductionmentioning

confidence: 99%

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Dimensionality reduction enhances data-driven reliability-based design optimize

Kanno

2020

JAMDSM

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A recently proposed data-driven approach to reliability-based design optimization of structures constructs a sufficient condition that the target reliability is guaranteed with the specified confidence level, without relying on any assumptions on statistical information of random variables. In general, there exists a gap between this sufficient condition and the original confidence-level constraint. This paper presents a simple dimensionality reduction technique that can possibly mitigate this gap. This technique is applied to the compliance constraint with the uncertain external load. Numerical experiments on truss and continuum examples demonstrate that the proposed method can drastically reduce the over-conservativeness of the original method.

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Section: Introductionmentioning

confidence: 73%

“…In this section, we formulate the design optimization problem dealt with in this paper, and briefly recall the methodology proposed in Kanno (2019).…”

Section: Problem Setting and Overview Of Data-driven Reliability-basementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dimensionality reduction enhances data-driven reliability-based design optimize

Kanno

2020

JAMDSM

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Robust geometry and topology optimization of plane frames using order statistics and force density method with global stability constraint

Shen

Ohsaki

Yamakawa

2021

Numerical Meth Engineering

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This paper presents a worst case approach for robust geometry and topology optimization of plane frames with global stability constraint. Uncertainty is assumed to exist in the nodal locations and cross‐sectional areas, and the worst values of the objective and stability constraint functions are relaxed to the quantile structural responses represented by the order statistics with given robustness and confidence levels. In order to alleviate the difficulty caused by melting nodes to some extent, the force density method is applied to an auxiliary truss model for geometry optimization of the frame, and the closely spaced nodes are merged. A method is presented for generating correlated imperfections for the nodal locations along each member, and a penalization approach is proposed for geometrical stiffness matrix to exclude superficial local buckling. It is demonstrated in the numerical examples that the result of robust optimization obtained by the proposed method is less sensitive to the uncertainty, and the stability constraint is also satisfied under uncertainty with the specified robustness and confidence levels.

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Computation‐with‐confidence for static elasticity: Data‐driven approach with order statistics

Kanno

2023

Z Angew Math Mech

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This paper presents a data‐driven method to identify a bound for the structural response of interest when a data set of elastic material responses is available. The obtained bound is probable in the sense that, at least with a certain confidence level, the probability that the structural response is within the bound is no smaller than a certain target value of reliability. The confidence level is ensured by considering the order statistics of the given data points. In this manner, this paper introduces the concept of reliability to the framework of data‐driven computational elasticity to address uncertainty in the data set. The method does not require any statistical information of the data points. Two numerical examples demonstrate the use of the proposed method to analyze static responses of structures by using only given material data sets.

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A data-driven approach to non-parametric reliability-based design optimization of structures with uncertain load

Cited by 17 publications

References 53 publications

Dimensionality reduction enhances data-driven reliability-based design optimize

Dimensionality reduction enhances data-driven reliability-based design optimize

Robust geometry and topology optimization of plane frames using order statistics and force density method with global stability constraint

Computation‐with‐confidence for static elasticity: Data‐driven approach with order statistics

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