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This nonlinear performance can be explained in terms of computational complexity as follows.
According to seismic design codes, nonlinear performance of structures is considered during strong earthquakes.
A cost function is considered as a nonlinear performance measure for the closed-loop system.
The stochastic damage model is adopted to describe the nonlinear performance and the randomness of concrete.
However, it may produce unstable results as periodic and chaos solutions for highly nonlinear performance functions.
In this paper, parametric study on nonlinear performance of steel batten columns with and without concrete cores was conducted.
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These pedals exhibit different nonlinear performances, as investigated below.
The proposed method is able to predict the nonlinear performances of underground structures under horizontal earthquake loadings.
The remarkable predominance indicates that the ensemble of support vector regression, moving least square, Kriging and artificial neural network holds great potential in approximating highly nonlinear performances for RBDO of automotive body structure.
Several numerical simulations are presented at the end of the paper, whose results allow for validating the capability of the proposed model for reproducing the typical nonlinear performances of concrete structures under different monotonic and cyclic load conditions.
In this paper, support vector regression, along with other four complex metamodeling techniques including moving least square, artificial neural network, radial basis function and Kriging, is investigated for approximating frontal crashworthiness performance which is one of the most highly nonlinear performances.
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