A novel mixed uncertainty support vector machine method for structural reliability analysis

You, Lingfei; Zhang, Jianguo; Zhou, Shuang; Wu, Jie

doi:10.1007/s00707-020-02906-1

Cited by 14 publications

(5 citation statements)

References 49 publications

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“…This loop is repeated until iteration termination. Finally, the best Random Forest (RF) [41], Gradient Boosting Decision Trees (GBDT) [42], Light GBM (LightGBM) [43], Ridge Regression (Ridge) [44], Linear Regression (LR) [27], Support Vector Regression (SVR) [28], Extreme Gradient Boosting (XGB) [45], and Extremely Randomized Trees (Extra Trees) [46].…”

Section: Intelligent Adaptive Stacking Methodsmentioning

confidence: 99%

“…It establishes a mapping relationship between design variables and responses and uses non-gradient optimization algorithms to search for the best solutions. Traditional surrogate models include the Kriging model [25], response surface method [26], linear regression [27], and support vector regression [28]. These models are mostly used for addressing simple predictive problems, while ensemble techniques have been developed in recent years to deal with complex blackbox problems [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing Vehicle Joints through Adaptive Stacking Model and Discrete Marine Predator Optimization Algorithm

Zhang

et al. 2023

Preprint

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The design of vehicle body joints is a critical aspect of the conceptual design process. Joint structures significantly affect the mechanical performance of vehicle bodies. However, due to the nonlinear relationship between joints and body performance, it is challenging to develop an explicit expression for optimization. Furthermore, traditional finite element analysis is impractical due to the vast number of possible joint configurations. Therefore, we propose a surrogate model-based optimization method to address this problem. First, we propose an intelligent adaptive stacking method (IASM) to establish the surrogate model. We evaluate the performance of IASM and other competitors on 34 benchmark functions and 3 open engineering projects, and IASM demonstrates the best predictive performance overall. Next, we construct joint modules with different configurations as candidate modules, which we connect to the vehicle body using beam units to build the simplified vehicle body (JMBB). JMBB significantly reduces the computational cost of finite element simulation, generating training samples for IASM. We then propose a discrete marine predator algorithm (DAMPA) to optimize the joints based on IASM. Compared to the genetic algorithm, DAMPA identifies joint modules with better mechanical performances. To validate the effectiveness of our method, we modify the base vehicle body using the optimized joints, resulting in a 7.4 kg reduction in body mass while enhancing four other mechanical metrics.

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Section: Intelligent Adaptive Stacking Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing Vehicle Joints through Adaptive Stacking Model and Discrete Marine Predator Optimization Algorithm

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In order to solve the problem, many surrogate model methods are widely used for reliability problems in recent years. The commonly used surrogate model methods are RSM, 24,25,39 support vector machine, 27 Kriging surrogate model, 26,[28][29][30][31] and ANN. 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.…”

Section: Surrogate Model Methodsmentioning

confidence: 99%

Section: 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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“…While it is easy to combine FEA with the MCS method to conduct reliability analysis, this method is computationally expensive. In recent years, metamodeling techniques have been developed to overcome this issue, such as the model tree (MT), evolutionary polynomial regression (EPR), multivariate adaptive regression spline (MARS), gene expression programming (GEP) [15], response surface method (RSM) [16][17][18], support vector machine [19,20], kriging surrogate model [21][22][23][24], and ANN [25][26][27][28][29][30][31][32]. Metamodeling techniques are adopted to establish the approximate models, which can replace the original implicit LSF.…”

Section: Introductionmentioning

confidence: 99%

Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method

2021

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This research aims to evaluate the calculation accuracy and efficiency of the artificial neural network-based important sampling method (ANN-IS) on reliability of structures such as drum brakes. The finite element analysis (FEA) result is used to establish the ANN sample in ANN-based reliability analysis methods. Because the process of FEA is time-consuming, the ANN sample size has a very important influence on the calculation efficiency. Two types of ANNs used in this study are the radial basis function neural network (RBF) and back propagation neural network (BP). RBF-IS and BP-IS methods are used to conduct reliability analysis on training samples of three different sizes, and the results are compared with several reliability analysis methods based on ANNs. The results show that the probability of failure of the RBF-IS method is closer to that of the Monte-Carlo simulation method (MCS) than those of other methods (including BP-IS). In addition, the RBF-IS method has better calculation efficiency than the other methods considered in this study. This research demonstrates that the RBF-IS method is well suited to structure reliability problems.

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A novel mixed uncertainty support vector machine method for structural reliability analysis

Cited by 14 publications

References 49 publications

Optimizing Vehicle Joints through Adaptive Stacking Model and Discrete Marine Predator Optimization Algorithm

Optimizing Vehicle Joints through Adaptive Stacking Model and Discrete Marine Predator Optimization Algorithm

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

Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method

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