A hybrid self-adjusted single-loop approach for reliability-based design optimization

Li, Xiaolan; Meng, Zeng; Chen, Guohai; Yang, Dan

doi:10.1007/s00158-019-02291-x

Cited by 23 publications

(5 citation statements)

References 46 publications

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“…The last example is from Reference 50 to examine the applicability of EMRIA to multiple design variables and constraints problems, which has 10 random design variables

{\overrightarrow{X}}_N,N=1,\dots, 10

and eight probabilistic constraints. Here, each random variable obeys the independent normal distribution

{\overrightarrow{X}}_N\sim N\left({\overrightarrow{d}}_N,{\sigma}_N\right)

while the desired reliability level is given as

{\beta}_i^t=3.0

.…”

Section: Validation Of the Emria For Rbdomentioning

confidence: 99%

Enhanced modified reliability index approach for efficient and robust reliability‐based design optimization

Shi

2022

Numerical Meth Engineering

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The reliability index approach (RIA) is one of the effective tools for solving the reliability‐based design optimization (RBDO) probabilistic model, which models the uncertainties with probability constraints. However, its wide application in engineering is limited due to low efficiency and convergence problems. The RIA‐based modified reliability index approach (MRIA) appears to be very robust and accurate than RIA but yields inefficient for the most probable point (MPP) search with highly nonlinear probabilistic constraints. In this study, an enhanced modified reliability index approach (EMRIA) is developed to improve the efficiency and robustness of searching for MPP and is utilized for RBDO. In the EMRIA, an innovative active set using rigorous inequality is applied to construct the region of exploring for MPP, where the unnecessary probabilistic constraint could be eliminated adaptively during the iterative process. Moreover, the double loop strategy (DLS) is integrated into the EMRIA to strengthen the efficiency and robustness of large‐scale RBDO problems. Two numerical examples demonstrated that the EMRIA is an efficient and robust method for MPP search in comparison with current first‐order reliability methods. Six RBDO problems quoted also indicate that DLS‐based EMRIA has good performance to solve complex RBDO problems.

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“…The last example is from Reference 50 to examine the applicability of EMRIA to multiple design variables and constraints problems, which has 10 random design variables

{\overrightarrow{X}}_N,N=1,\dots, 10

and eight probabilistic constraints. Here, each random variable obeys the independent normal distribution

{\overrightarrow{X}}_N\sim N\left({\overrightarrow{d}}_N,{\sigma}_N\right)

while the desired reliability level is given as

{\beta}_i^t=3.0

.…”

Section: Validation Of the Emria For Rbdomentioning

confidence: 99%

Enhanced modified reliability index approach for efficient and robust reliability‐based design optimization

Shi

2022

Numerical Meth Engineering

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“…The mono-level (or single-loop approach) is used to refer to methods that attempt to avoid the RA altogether by a reformulation of the RBDO problem, such as in Chen et al's single-loop approach (SLA) [12,38,46], or by replacing the RA by optimality conditions, such as Kuschel & Rackwitz's Karush-Kuhn-Tucker (KKT) approach [40]. By doing so, both reliability analysis and design optimization converge in parallel to the optimal solution.…”

Section: Reliability-based Design Optimizationmentioning

confidence: 99%

ESLA: a new surrogate-assisted single-loop reliability-based design optimization technique

Wauters

Couckuyt

Degroote

2021

Struct Multidisc Optim

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In this paper, we address the formulation of a novel scheme for reliabilitybased design optimization, in which the design optimization problem is characterized by constraints that must be met with a certain probability. Assessment of the aforementioned is typically referred to as reliability analysis. Conventional methods rely on sampling approaches or by reformulating the problem as a two-level optimization that requires gradient or Hessian information of the constraints to obtain a trustworthy solution. However, the computational cost of such methods makes them often impractical. To overcome the aforementioned, a surrogateassisted asymptotic reliability analysis (SARA) is presented that makes use of surrogate-derived gradient and Hessian information. The sub-optimization problem is reformulated as a set of constraints using the Karush-Kuhn-Tucker conditions and fitted in an efficient global optimization-like setting through the formulation of the reliability-based expected improvement (RBEI), obtaining the novel efficient single-loop approach (ESLA). The method is tested on a series of test cases which prove the effectiveness of the novel scheme.

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“…For such group of approaches, we refer. 12,13 The aforementioned RBDO approaches mainly cope with the situation where the parametric uncertainties are timeindependent. However, it is a common sense that the time-dependent uncertainties are pervasive in practical engineering systems, such as the material characteristics or geometry size degenerating over time and the time-dependent stochastic excitations.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it is usually accompanied by the existence and uniqueness assumptions of MPTP. For such group of approaches, we refer 12,13 …”

Section: Introductionmentioning

confidence: 99%

A global strategy based on deep learning for time‐dependent optimal reliability design

Ling

Kuo

2023

Quality & Reliability Eng

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Time‐dependent reliability‐based design optimization (RBDO) is a computationally tough problem that needs to be addressed urgently. The difficulty of solving the time‐dependent RBDO mainly comes from the time‐dependent reliability analysis involved in probabilistic constraints, which itself is one of the thorny problems in the reliability community and makes the computational cost become much more onerous. In this paper, a deep‐learning‐assisted approach is proposed to solve the time‐dependent RBDO. The proposed approach leverages the classification capability of the deep learning, and constructs the alternative model for the actual probabilistic constraint function in the so‐called augmented reliability space, so as to make the trained alternative model accurate wherever it will be invoked. Moreover, a sequential sampling technique utilizing the classification probability provided by the deep learning is proposed to further reduce the computational cost. Then, the time‐dependent reliability analysis involved in the time‐dependent RBDO is conducted by the cheaper alternative model instead of the original computing‐intensive probabilistic constraint function, which evidently reduces the computational burden. The presented examples showcase the performance of the proposed approach. Especially, for the complicated engineering application, the proposed approach saves about 10% of the computational cost compared with the existing methods.

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A hybrid self-adjusted single-loop approach for reliability-based design optimization

Cited by 23 publications

References 46 publications

Enhanced modified reliability index approach for efficient and robust reliability‐based design optimization

Enhanced modified reliability index approach for efficient and robust reliability‐based design optimization

ESLA: a new surrogate-assisted single-loop reliability-based design optimization technique

A global strategy based on deep learning for time‐dependent optimal reliability design

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