High-order limit state functions in the response surface method for structural reliability analysis

MLS Based Sequential SRSM in Sparse Grid Framework for Efficient Uncertainty Quantification

“…The results of uncertainty quantification are presented in Fig. 3 and Table 1 [12]. This demonstrates the efficiency of the proposed method.…”

The seismic performance reliability of reinforced concrete moment structures

“…In terms of efficiency, polynomial order should be selected as the number of deterministic structural analyses is reduced for calculation of RSF parameters, which is important in problems with many random variables. In addition, it may be led to an illcondition system of equations when using high-order polynomials [2]. Accordingly, a second-order polynomial with interaction terms was applied in this study:…”

“…Response parameters of structure are calculated at these points with Non-Linear Dynamic Analysis (NLDA); then, RSF is fitted as Eq. (2). Reliability index (β), design point (DP) and relative importance of random variables are calculated for the fitted RSF through FORM.…”

“…It was shown that selection of experimental points affects RSF approximation and corresponding failure probability significantly [1]. Application of higher order polynomials to approximate LSF was indicated in [2]. A non-constant order polynomial was used for LSF and order of this polynomial was determined with statistical analysis of coefficients.…”

Quantitative functional failure analysis of a thermal–hydraulic passive system by means of bootstrapped Artificial Neural Networks

“…Several examples can be found in the open literature concerning the application of surrogate meta-models in reliability problems. In (Bucher and Most, 2008;Gavin and Yau, 2008;Liel et al, 2008), polynomial Response Surfaces (RSs) are employed to evaluate the failure probability of structural systems; in Fong et al, 2009;Mathews et al, 2009), linear and quadratic polynomial RSs are employed for performing the reliability analysis of T-H passive systems in advanced nuclear reactors; in (Deng, 2006;Hurtado, 2007;Cardoso et al, 2008;Cheng et al, 2008), learning statistical models such as Artificial Neural Networks (ANNs), Radial Basis Functions (RBFs) and Support Vector Machines (SVMs) are trained to provide local approximations of the failure domain in structural reliability problems; in (Volkova et al, 2008;Marrel et al, 2009), Gaussian meta-models are built to calculate global sensitivity indices for a complex hydrogeological model simulating radionuclide transport in groundwater.…”