Analytical robust design optimization based on a hybrid surrogate model by combining polynomial chaos expansion and Gaussian kernel

Liu, Ye; Zhao, Gang; Li, Gang; He, Wanxin; Zhong, Changting

doi:10.1007/s00158-022-03400-z

Cited by 10 publications

(3 citation statements)

References 51 publications

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“…As a recently emerged stochastic expansion method, polynomial chaos expansion (PCE) exhibits a stronger nonlinear approximation capability than the simple RSM. In addition, compared to commonly used machine learning algorithms such as artificial neural networks and SVR, PCE does not require extensive effort to find the optimal hyperparameters and can achieve the desired accuracy with fewer training samples (Y. Liu et al., 2022). These advantages make PCE suitable for meta‐modeling displacements in high arch dams.…”

Section: Proposed Methodologymentioning

confidence: 99%

Inverse analysis of deformation moduli for high arch dams using the displacement reconstruction technique and multi‐objective optimization

Li,

Wu,

Chen

et al. 2023

Computer aided Civil Eng

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The inverse analysis of the deformation moduli of high arch dams based on displacement monitoring data is essential for structural safety assessment. In traditional inverse analysis methods, the deformation moduli are identified based on the single‐objective optimization and the hydrostatic component derived from the statistical model. This type of method has two main shortcomings: First, it treats the essential multi‐objective optimization problem as a single‐objective problem; second, the extracted hydrostatic component may be biased due to the multicollinearity of variables in the statistical model. This paper presents a methodology for the inverse analysis of the deformation moduli of high arch dams under a multi‐objective optimization strategy. The methodology employs empirical mode decomposition to extract the aging component from displacement monitoring data. Then, thermomechanical analysis is used to reconstruct the remaining hydrostatic and temperature components, thereby avoiding the biases encountered in solving the statistical model. The adaptive polynomial chaos expansion method is embedded in the NSGA‐III algorithm to establish and solve multi‐objective functions in the inverse analysis. Additionally, a composite decision index considering errors and test information is proposed to determine acceptable deformation moduli from the Pareto solution set. A high arch dam is selected to illustrate this methodology with static and dynamic monitoring data. The results show that the identified deformation moduli have errors of 3.8% and 7.2% in displacement and acceleration, respectively. The proposed methodology can yield deformation modulus values that are more consistent with the physical implications than those of the single‐objective optimization method.

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Section: Proposed Methodologymentioning

confidence: 99%

Inverse analysis of deformation moduli for high arch dams using the displacement reconstruction technique and multi‐objective optimization

Li,

Wu,

Chen

et al. 2023

Computer aided Civil Eng

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“…Its inner scale, s, indicates that its extent is Gaussian weighted. The GK is defined as follows in one-dimensional, two-dimensional, and neuronal dimensions [21]: www.ijacsa.thesai.org…”

Section: ) Gaussian Kernel (Gk)mentioning

confidence: 99%

A Comparative Work to Highlight the Superiority of Mouth Brooding Fish (MBF) over the Various ML Techniques in Password Security Classification

Shi,

Wang

2024

ijacsa

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Within the domain of password security classification, the pursuit of practical and dependable methodologies has prompted the examination of both biological and technological paradigms. The present study investigates the efficacy of Mouth Brooding Fish (MBF) as an innovative method in contrast to conventional Machine Learning (ML) approaches for classifying password security. The research approach entails a rigorous examination of the comparative analysis of MBF and ML algorithms, evaluating their effectiveness in password classification using many criteria, including accuracy, robustness, flexibility, and durability against adversarial assaults. The findings suggest that ML approaches have shown significant effectiveness in classifying passwords. However, using methodologies inspired by the minimum Bayes risk framework demonstrates a higher degree of resistance against typical cyber dangers. The intrinsic biological mechanisms of MBF, encompassing adaptive behaviors and inherent protection, play a role in enhancing the resilience and adaptability of the password security categorization system. The results offer significant insights that can inform the evolution of password security systems, integrating biological principles with technical progress to enhance safeguarding measures in digital environments. To emphasize the advantages of the suggested approach, several ML approaches are investigated, such as Support Vector Machines (SVM), AdaBoost, Multilayer Perceptron (MLP), Gaussian Kernel (GK), and Random Forest (RF). The F-score, accuracy, sensitivity, and specificity metrics for MBF exhibit noteworthy performance compared to the other selected models, with values of 100%.

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“…Sun et al studied the construction method of a mixed substitution model based on second-order polynomial response surface models (PRSMs), radial basis functions (RBFs), and Kriging lattice substitution models for the multiparameter optimization problem involved in the pulse jet cleaning process of bag flters [20]. Liu et al proposed a new alternative model PC-GK-SBL, which combines polynomial chaotic expansion (PCE) and Gaussian kernel (GK), under the sparse Bayesian learning (SBL) framework, signifcantly improving computational efciency [21]. Denimal et al combined the Kriging form with generalized polynomial chaos to predict friction-induced instability with interval and probability uncertainties [22].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization of Composite Material Seat Plate for Mortar Based on the Hybrid Surrogate Model

Wang,

Xu,

2024

Shock and Vibration

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As an important force transmission component of mortars, the seat plate affects some core indicators of mortars such as range, shooting accuracy, and maneuverability. In order to withstand huge impact loads, the seat plate was previously made of metal, which accounts for approximately 30%–45% of the total weight of the gun. The drawbacks of the heavy weight of the seat plate, which are not conducive to transportation and transfer, run counter to the current direction of the mortar’s lightweight development. The application of composite materials can greatly reduce the weight of the seat plate, but it exacerbates the contradiction between the mobility and combat effectiveness of mortars. In order to achieve the best match between mortar stability and maneuverability, a multiobjective optimization of composite material layers for seat plates is proposed, utilizing the designability of composite material layers. First, a fiber continuity model based on dropout sequence is adopted to solve the problems existing in the design of inherent continuity classes for composite layered fibers. Second, a hybrid surrogate model that considers the composite material seat plate quality, structural strength, shooting stability, shooting accuracy, and various working conditions is considered. Then, in order to improve the optimization efficiency and robustness of the algorithm, a multiobjective optimization algorithm based on the Chebyshev combination pattern is used to solve the mixed surrogate model. Finally, the optimization results are comprehensively evaluated against the optimization objectives. Research has shown that the method proposed in this article can effectively solve the time-consuming problem of multiobjective optimization, improve the accuracy of hybrid surrogate models, and meet the expected requirements of multiobjective optimization of composite material seat plates. While ensuring shooting stability, the weight of the seat plate is reduced by 18.43% compared to the metal seat plate, which has important application value for lightweight design of mortars.

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Analytical robust design optimization based on a hybrid surrogate model by combining polynomial chaos expansion and Gaussian kernel

Cited by 10 publications

References 51 publications

Inverse analysis of deformation moduli for high arch dams using the displacement reconstruction technique and multi‐objective optimization

Inverse analysis of deformation moduli for high arch dams using the displacement reconstruction technique and multi‐objective optimization

A Comparative Work to Highlight the Superiority of Mouth Brooding Fish (MBF) over the Various ML Techniques in Password Security Classification

Multiobjective Optimization of Composite Material Seat Plate for Mortar Based on the Hybrid Surrogate Model

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