Ensemble of surrogates combining Kriging and Artificial Neural Networks for reliability analysis with local goodness measurement

Ren, Chao; Aoues, Younès; Lemosse, Didier; Cursi, Eduardo Souza de

doi:10.1016/j.strusafe.2022.102186

Cited by 49 publications

(7 citation statements)

References 41 publications

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“…8,22,23,32 Based on these theories, researches propose new ideas and advanced surrogate model methods such as VSM method, 11 deep learning model, [33][34][35] and advanced Kriging method. 12,36 In this subsection, several advanced surrogate model methods are briefly reviewed.…”

Section: Surrogate Model Methodsmentioning

confidence: 99%

A reliability‐based robust optimization design for the drum brake using adaptive Kriging surrogate model

Yang

Pak

Kwon

et al. 2022

Quality & Reliability Eng

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To decrease random parameters’ influence on the drum brake reliability, the reliability‐based robust optimization design (RBROD) of the electric vehicle brake is proposed. Based on the assumption that the maximum temperature of the brake cannot exceed the allowable temperature, a performance function model of thermal–mechanical coupling reliability of drum brakes is established by the adaptive Kriging method, and the analysis of reliability sensitivity and RBROD are conducted. The accuracy of the proposed model is verified by temperature measurement experiment under emergency braking condition. The robust optimization design improves the drum brake reliability to 0.99998 and reduce the influence of the design parameters on the reliability, with the absolute values of the reliability sensitivity and the weight of the drum brake are significantly smaller. Therefore, the objectives of reliability design, robustness design, and optimization design are simultaneously achieved by the proposed methods. Besides, the relative error of the proposed method is 0.373%, the number of function evaluations is 39, and the comparison with four meta‐model methods show that the proposed method holds high‐accuracy and high‐efficiency. This study provides a high‐precision theoretical explanation for the robust optimization design of drum brake.

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Section: Surrogate Model Methodsmentioning

confidence: 99%

A reliability‐based robust optimization design for the drum brake using adaptive Kriging surrogate model

Yang

Pak

Kwon

et al. 2022

Quality & Reliability Eng

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“…The results show that their method works efficiently and more accurately than the commonly used response surface methodology. Ren et al used the ensemble of surrogates with ANN and Kriging to solve the challenge of reliability evaluation with limited knowledge of the LSF [50]. Then merits of both two models can be captured.…”

Section: Mlp-based Mcsmentioning

confidence: 99%

Deep learning-based methods in structural reliability analysis: a review

Afshari

Zhao

Zhuang

et al. 2023

Meas. Sci. Technol.

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One of the most significant and growing research fields in mechanical and civil engineering is Structural Reliability Analysis (SRA). A reliable and precise SRA usually has to deal with complicated , aand numerically expensive problems. Artificial intelligence-based (AI) nd specifically, Deep learning-based (DL) methods, have been applied to the SRA problems to reduce the computational cost and to improve the accuracy of reliability estimation as well. This article reviews the recent advances in using DL models in SRA problems. The review includes the most common categories of DL-based methods used in SRA. More specifically, the application of supervised methods, unsupervised methods, and hybrid deep learning methods in SRA are explained. In this paper, the supervised methods for SRA are categorized as Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN ), Long short-term memory (LSTM), Bidirectional (Bi-LSTM) and Gated recurrent units (GRU). For the unsupervised methods, we have investigated methods such as Generative Adversarial Network (GAN), Autoencoders (AE), Self-Organizing Map (SOM), Restricted Boltzmann Machine (RBM), and Deep Belief Network (DBN). We have made a comprehensive survey of these methods in SRA. Aiming towards an efficient SRA, deep learning-based methods applied for approximating the limit state function (LSF) with First/Second Order Reliability Methods (FORM/SORM), Monte Carlo simulation (MCS), or MCS with importance sampling (IS). Accordingly, the current paper focuses on the structure of different DL-based models and the applications of each DL method in various SRA problems. This survey helps researchers in mechanical and civil engineering, especially those who are engaged with structural and reliability analysis or dealing with quality assurance problems.

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“…To avoid this pitfall, in this work we resort to the solution of ensemble modeling [74,75]. That is, the original dataset is randomly re-ordered I times, thus generating training and test datasets D .…”

Section: Model Uncertainty and Ensemble Modelingmentioning

confidence: 99%

Power Module Heat Sink Design Optimization with Ensembles of Data-Driven Polynomial Chaos Surrogate Models

Loukrezis¹,

Gersem²

2022

Preprint

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We consider the problem of optimizing the design of a heat sink used for cooling an insulated gate bipolar transistor (IGBT) power module. The thermal behavior of the heat sink is originally estimated using a high-fidelity computational fluid dynamics (CFD) simulation, which renders numerical optimization too computationally demanding. To enable optimization studies, we substitute the CFD simulation model with an inexpensive polynomial surrogate model that approximates the relation between the device's design features and a relevant thermal quantity of interest. The surrogate model of choice is a data-driven polynomial chaos expansion (DD-PCE), which learns the aforementioned relation by means of polynomial regression.Advantages of the DD-PCE include its applicability in small-data regimes and its easily adaptable model structure. Once trained and tested in terms of prediction accuracy, the DD-PCE replaces the high-fidelity simulation model in optimization algorithms aiming to identify heat sink designs that optimize the thermal behavior of the IGBT under geometrical and operational constraints. Optimized heat sink designs are obtained for a computational cost much smaller than utilizing the original model in the optimization procedure. Comparisons against alternative surrogate modeling techniques illustrate why the DD-PCE should be preferred in the considered setting.

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Ensemble of surrogates combining Kriging and Artificial Neural Networks for reliability analysis with local goodness measurement

Cited by 49 publications

References 41 publications

A reliability‐based robust optimization design for the drum brake using adaptive Kriging surrogate model

A reliability‐based robust optimization design for the drum brake using adaptive Kriging surrogate model

Deep learning-based methods in structural reliability analysis: a review

Power Module Heat Sink Design Optimization with Ensembles of Data-Driven Polynomial Chaos Surrogate Models

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