RBF surrogate model and EN1317 collision safety-based optimization of two guardrails

Özcanan, Sedat; Atahan, Ali Osman

doi:10.1007/s00158-019-02203-z

Cited by 21 publications

(12 citation statements)

References 47 publications

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“…One of the most critical steps in surrogate-based optimization is the construction of the surrogate model by values on the sampling points. Here, the radial basis function (RBF) model 45,46 is adopted for interpolation and fitting of the surrogate model. With linear combinations of a radially symmetric function based on Euclidean distance, the RBF model can be mathematically expressed as 45…”

Section: Analysis Methodsmentioning

confidence: 99%

Optimization design of curved outrigger structure based on buckling analysis and multi-island genetic algorithm

et al. 2021

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In the present work, the working state of the crane leg is analyzed and discussed, and its structure is optimized. SolidWorks software is used for modeling; ANSYS software is used for finite element analysis. First of all, the constrained finite element method (CFEM) is used to analyze the linear eigenvalue buckling and geometric nonlinear buckling of outriggers with different cross-section shapes. Prove that the curved leg has certain advantages in buckling. At the same time, analyzing the leg along a different path of buckling condition and stress changes provide the basis for the design of the subsequent reinforcement. After selecting the best cross-section shape of the outrigger, the agent-based multi-island genetic algorithm is used to optimize the structural parameters of the outrigger under the transverse stiffened plate reinforced structure and the longitudinally stiffened plate reinforced structure respectively. It is proved that the outrigger with the transverse stiffened plate has a significant effect in improving the bearing capacity and in the lightweight of the structure. Finally, the gap between the movable leg and the fixed leg was changed, the stress of different gaps was analyzed by using the finite element method, and the appropriate gap value was selected according to the high-order fitting curve.

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Section: Analysis Methodsmentioning

confidence: 99%

Optimization design of curved outrigger structure based on buckling analysis and multi-island genetic algorithm

et al. 2021

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“…It provides an efficient way of predicting the responses of new design alternatives without running additional simulations [4]. Surrogate-based methods have been applied successfully in engineering practices, for example, vehicle crashworthiness design [5], crane bridge optimization [6], transportation facility design [7] and so on. To be adapted to various engineering problems, which are characterized by a different number of design variables, degree of nonlinearities, and loading rates and so on, several surrogate models ha ve been proposed, such as polynomial response surface model, radial basis function and Kriging model [5].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Effect of Hyperparameter Optimization on Machine Learning Models for Structure Design Problems

Zhu

2021

Computer-Aided Design

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“…For complex engineering problems, sometimes the performance function is implicit, or due to cost and time limit, the surrogate model is often applied to approximate the real physical model. Commonly used surrogate models mainly include Kriging [1], artificial neural network [2], radial basis function (RBF) [3], support vector regression(SVR) [4], and polynomial response surface(PRS) [5].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble of Adaptive Surrogate Models Based on Local Error Expectations

Zhang

et al. 2021

Mathematical Problems in Engineering

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An ensemble of surrogate models with high robustness and accuracy can effectively avoid the difficult choice of surrogate model. However, most of the existing ensembles of surrogate models are constructed with static sampling methods. In this paper, we propose an ensemble of adaptive surrogate models by applying adaptive sampling strategy based on expected local errors. In the proposed method, local error expectations of the surrogate models are calculated. Then according to local error expectations, the new sample points are added within the dominating radius of the samples. Constructed by the RBF and Kriging models, the ensemble of adaptive surrogate models is proposed by combining the adaptive sampling strategy. The benchmark test functions and an application problem that deals with driving arm base of palletizing robot show that the proposed method can effectively improve the global and local prediction accuracy of the surrogate model.

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RBF surrogate model and EN1317 collision safety-based optimization of two guardrails

Cited by 21 publications

References 47 publications

Optimization design of curved outrigger structure based on buckling analysis and multi-island genetic algorithm

Optimization design of curved outrigger structure based on buckling analysis and multi-island genetic algorithm

Understanding the Effect of Hyperparameter Optimization on Machine Learning Models for Structure Design Problems

An Ensemble of Adaptive Surrogate Models Based on Local Error Expectations

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