Structural reliability analysis using Monte Carlo simulation and neural networks

Review and application of Artificial Neural Networks models in reliability analysis of steel structures

“…Despite the significant reduction of the computational time, the method is still time consuming and, for this reason, several variance reduction techniques, such as importance sampling or directional simulation, have been widely applied. An extensive description, as well as, numerical examples of the use of ANN-based MCS in structural reliability can be found in ( [31], [32], [27], [33], [34]). …”

“…Cardoso et al [27] have shown that ANN is a versatile methodology that can approximate accurately highly non-linear functions over the entire domain with very good precision. Several studies have also been performed showing the accuracy and efficiency of the ANN-based response surface method for reliability assessment in comparison with the conventional response surface methods.…”

Novel procedure for reliability-based cost optimization of seismically isolated structures for the protection of critical equipment: A case study using single curved surface sliders

“…This concept was introduced by McCulloch and Pitts, who proposed a mathematical model to simulate neuron behavior. [58] Nowadays, NNs feature in many diverse practical applications. Their wide applicability is displayed in numerous of computational structure applications that require extensive computer resources.…”

How to effectively compute the reliability of a thermal–hydraulic nuclear passive system

“…In [Bucher and Most, 2008;Gavin and Yau, 2008;Liel et al, 2009], 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.…”