A New Reliability Analysis Method Combining Adaptive Kriging With Weight Index Monte Carlo Simulation

Ma, Jiangang; Ren, Ziyan; Zhao, Guoxin; Koh, Chang-Soon

doi:10.1109/tmag.2017.2763198

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…where 𝐺 𝑖 = (𝐺 𝑖+0.5 + 𝐺 𝑖−0.5 )∕2. According to the composite Simpson's rule 39,40 of numerical integration,…”

Section: Modified Wlds Using Composite Simpson's Rulementioning

confidence: 99%

Geotechnical reliability‐based design optimization updating under changing design scenarios

Wang,

2024

Num Anal Meth Geomechanics

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In the context of geotechnical uncertainty characterization, the reliability‐based design optimization (RBDO) provides an effective means to achieve the most balanced design outcome between the targeted safety and the risk‐based cost planning. As a common practice, the geotechnical RBDO is conducted on the premise of prescribed statistical information of those uncertain parameters. Nevertheless, the uncertain geotechnical parameters are usually subject to amendment resulting from further site investigation. In other words, changing design scenarios must be properly handled in advanced geotechnical RBDO. Thus, in this study, a novel reliability updating method using a sample reweighting technique that requires only a single simulation run is developed for RBDO. Simultaneously, the RBDO is readily extendable to RBDO updating under changing design scenarios while avoiding any further re‐evaluation of the performance functions. The proposed RBDO updating method allows for the consideration of a continuous design space encompassing an infinite number of feasible designs, whereas the existing simulation‐based method can only consider a finite number of feasible designs in a discrete design space. Moreover, the efficiency and flexibility of the proposed RBDO updating method are illustrated through a standard RBDO test problem and a practical rock slope design example, where the changing design scenarios of different means, standard deviations, correlation coefficient, and distributions are respectively studied.

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“…where 𝐺 𝑖 = (𝐺 𝑖+0.5 + 𝐺 𝑖−0.5 )∕2. According to the composite Simpson's rule 39,40 of numerical integration,…”

Section: Modified Wlds Using Composite Simpson's Rulementioning

confidence: 99%

Geotechnical reliability‐based design optimization updating under changing design scenarios

Wang,

2024

Num Anal Meth Geomechanics

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“…In this context, most of the recent literature has emphasized the effective use of metamodel-assisted approaches to enhance performances, for which the computational cost of some complex engineering models is reduced by an inexpensive surrogate [7]. Specifically, several Kriging-assisted approaches have been proposed in the literature for nested loops RBDO problems, in which the time-consuming limit-state functions (LSF) are replaced with Kriging model, [8,9,10]. The use of Kriging (a.k.a.…”

Section: Literature Contextmentioning

confidence: 99%

An Unified Enrichment Strategy for Reliability-Based Design Optimization Using Adaptive Kriging and Zero-Order Algorithms

Faraci,

Beaurepaire,

Gayton

2023

5th International Conference on Uncertainty Quantification in Computational Sciences and Engineering

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The optimal design of complex engineering systems is a necessary task in a challenging industrial environment. Constrained optimization frameworks properly take charge of the engineering requirements by considering uncertainties in the constraint and identifying the optimal compound of design parameters among the admissible ones. However, this procedure is often very time-consuming and requires a significant number of calls 3 rd party solvers, especially when dealing with probabilities of rare events. The specific objective of this work is to propose a new unified enrichment strategy based on Adaptive Kriging and zero-order (gradientfree) optimization algorithms to identify efficiently the solution of Reliability-Based Design Optimization problems when using Monte Carlo Simulation based methods to assess reliability. This enables the parallel operation of the reliability computation and the optimization. As a result, the proposed procedure provides a significant improvement in the refinement of the Kriging surrogate around the most reliable design solution within an affordable computational-time cost.

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“…This might be a limiting factor even when local search is concerned (e.g., gradient-based procedures 13 ), whereas it is particularly troublesome for global optimization 14 , 15 , uncertainty quantification, UQ (e.g., statistical analysis 16 , yield maximization 17 ), or multi-criterial design 18 , 19 . Many of these tasks require hundreds and thousands of system evaluations when solved using conventional methods, such as Monte Carlo simulation in statistical design 20 , or direct EM-driven global optimization using bio-inspired population-based algorithms 21 , 22 . There have been plenty of methods developed to expedite simulation-based design procedures, including simplistic approaches, e.g., supervised parametric studies 23 , 24 (still widely used in practice), or worst-case analysis for UQ 25 .…”

Section: Introductionmentioning

confidence: 99%

Reduced-cost microwave modeling using constrained domains and dimensionality reduction

Koziel,

Pietrenko-Dabrowska,

Ullah

2023

Sci Rep

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Development of modern microwave devices largely exploits full-wave electromagnetic (EM) simulations. Yet, simulation-driven design may be problematic due to the incurred CPU expenses. Addressing the high-cost issues stimulated the development of surrogate modeling methods. Among them, data-driven techniques seem to be the most widespread owing to their flexibility and accessibility. Nonetheless, applicability of approximation-based modeling for real-world microwave components is hindered by a high nonlinearity of the system characteristics, dimensionality issues, and broad ranges of operating parameters the model should cover to make it practically useful. Performance-driven modeling frameworks deliver a partial mitigation of these problems through appropriate spatial orientation of the metamodel domain, which only encapsulates high-quality designs and not the entire space. Unfortunately, the initial model setup cost is high, as defining the domain requires database designs that need to be a priori acquired. This paper introduces a novel approach, where the database designs are replaced by random observables, and dimensionality of the domain is reduced using spectral analysis thereof. The major contributions of the work include implementation of the explicit dimensionality reduction of the confined surrogate model domain and introducing this concept into a complete cost-efficient framework for modeling of microwave components. Comprehensive benchmarking demonstrates excellent performance of the introduced framework, both in terms of predictive power of the rendered surrogates, their scalability properties, as well as low computational overhead associated with the model setup.

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A New Reliability Analysis Method Combining Adaptive Kriging With Weight Index Monte Carlo Simulation

Cited by 10 publications

References 13 publications

Geotechnical reliability‐based design optimization updating under changing design scenarios

Geotechnical reliability‐based design optimization updating under changing design scenarios

An Unified Enrichment Strategy for Reliability-Based Design Optimization Using Adaptive Kriging and Zero-Order Algorithms

Reduced-cost microwave modeling using constrained domains and dimensionality reduction

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