Approach for the structural reliability analysis by the modified sensitivity model based on response surface function - Kriging model

Zhu, Lin; Qiu, Jianchun; Chen, Min; Jia, Minping

doi:10.1016/j.heliyon.2022.e10046

Cited by 8 publications

(4 citation statements)

References 64 publications

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“…Due to its significant efficiency improvement in engineering optimization design problems, surrogate model methods are widely applied in engineering design. Commonly used surrogate models include the Standard Response Surface and Kriging (Zhang and Qiu, 2021;Zhu et al, 2022). Upon comparison, it has been found that the Kriging model demonstrates a better fit between the observed values at design points and the predicted values by the response surface.…”

Section: Optimization Methodsmentioning

confidence: 99%

Optimal sand transport roadbed geometry structure of Taklamakan Desert highway

Wang,

Cheng,

et al. 2024

Front. Earth Sci.

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In order to obtain the optimal geometric structure for sand transport in desert roads, this study fully utilizes the natural sand transport capacity of the desert roads, aiming to reduce occurrences of road damage due to sand burial. The research integrates Computational Fluid Dynamics (CFD) with optimization analysis theory. Various typical geometric structures of desert highway roadbed were modeled using the Design Exploration module in CFD. Optimization analysis methods were employed to model and compute the sediment transport optimization design on both the roadbed and road surface. Then, the initial sample points are obtained by using the Design of Experiments (DOE), and the response surface is established by using the Kriging model to obtain the change trend of the input parameters and the objective function. Finally, the cross-section parameters of the sediment transport subgrade corresponding to different inflow conditions are obtained. The results show that the sediment transport performance of embankment, cutting and semi-filled uphill subgrade is negatively correlated with the height of subgrade. The relationship between slope gradient and sediment transport performance of subgrade depends on the height of subgrade and the type of subgrade section. For embankment and cutting, when the subgrade height is less than 0.5 m, the sediment transport performance of the subgrade is positively correlated with the subgrade slope; however, when the subgrade height exceeds 0.5 m, the sediment transport performance is negatively correlated with the subgrade slope. For the semi-filled uphill flow subgrade, the sediment transport performance of the subgrade is negatively correlated with the subgrade slope. Comprehensive analysis shows that semi-filled and excavated downhill subgrade has the best sand transport performance, embankment subgrade has the second highest sand transport capacity, then cutting subgrades, while semi-filled and excavated uphill subgrade has the worst sand transport performance. The research conclusions provide valuable scientific guidance for the design of sand control embankment structures tailored to local conditions for desert highways. This is of significant importance for enhancing the sediment transport capacity of desert highways and prolonging their service life.

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Section: Optimization Methodsmentioning

confidence: 99%

Optimal sand transport roadbed geometry structure of Taklamakan Desert highway

Wang,

Cheng,

et al. 2024

Front. Earth Sci.

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“…The surrogate model-based optimization couples the optimization algorithms with surrogate models to ascertain optimal design parameters, offering the benefits of the simple model and high efficiency. Various surrogate models, such as response surface methodology (RSM) models [ 7 , 8 ] artificial neural network model (ANN) [ 9 ], and Kriging model [ 10 ], have been employed to depict the relationship between design parameters and objective function. On the other hand, the physical model-based optimization integrates the optimization algorithms with finite element models, eliminating the approximation errors inherent in surrogate models.…”

Section: Introductionmentioning

confidence: 99%

Optimizing heat source distribution in sintering molds: Integrating response surface model with sequential quadratic programming

Liu,

Chen,

Zhu

et al. 2024

Heliyon

Self Cite

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“…[30][31][32][33] Jensen 34 proposed a simple post-processing step associated with an advanced sampling-based reliability analysis to quantify the impact of individual input variable on the output parameter, which also validated on two nonlinear structural models under stochastic ground excitation. Zhu 35 studied a response surface function Kriging model with Sobol' sensitivity algorithm. Through the new corrected model, the impact of different factors on the structural reliability of port cranes are analyzed.…”

Section: Introductionmentioning

confidence: 99%

“…The quantitative evaluation of the influence degree between factors and reliability indexes can be realized, in the literature. [32][33][34][35] However, the interaction between factors cannot be reflected in the analysis results, due to the lack of global sensitivit consideration. [36][37][38] Therefore, it is very important to quantitatively evaluate the coupling relationship between reliability indicators and complex influencing factors precisely.…”

Section: Introductionmentioning

confidence: 99%

Influence of coupling factors on structural reliability based on polynomial response surface optimization model

Jiang

Wang

Chen

et al. 2023

Advances in Mechanical Engineering

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The sensitivity analysis model is frequently used to express the influence of conditional elements on structural reliability. However, the traditional sensitivity analysis model is limited to a few influencing factors, and has a small scope of application. In this paper, a modified sensitivity model is proposed by combining the optimal polynomial response surface function with the Sobol sensitivity algorithm. And the sensitivity calculation approach was combined with coupling factor test design, range verification, multi-body dynamics analysis and structural statics analysis, which enables to achieve the quantitative sensitivity value of the influence of conditional factors on structural reliability. Finally, a typical harvester structure is selected as a case, to verify the evaluation accuracy and effectiveness of the revised model. The results show that the sensitivity of threshing drum speed has the greatest influence, while the sensitivity of granary load has the least influence. The average quantitative prediction accuracy of revised approach is up to 97.36%. The revised model can accurately evaluate the sensitivity value of coupled influencing factors for industrial equipment.

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Approach for the structural reliability analysis by the modified sensitivity model based on response surface function - Kriging model

Cited by 8 publications

References 64 publications

Optimal sand transport roadbed geometry structure of Taklamakan Desert highway

Optimal sand transport roadbed geometry structure of Taklamakan Desert highway

Optimizing heat source distribution in sintering molds: Integrating response surface model with sequential quadratic programming

Influence of coupling factors on structural reliability based on polynomial response surface optimization model

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