Details

Autor(en) / Beteiligte

• Ghosh, Shyamal
• Roy, Atin
• Chakraborty, Subrata

Titel

Support vector regression based metamodeling for seismic reliability analysis of structures

Ist Teil von

• Applied Mathematical Modelling, 2018-12, Vol.64, p.584-602

Ort / Verlag

New York: Elsevier Inc

Erscheinungsjahr

2018

Quelle

EBSCOhost Business Source Ultimate

Beschreibungen/Notizen

• •Improved seismic reliability analysis by support vector regression based metamodel.•A simple effective algorithm to minimize the mean square error to obtain the model parameters.•Generating a suite of metamodels to implicitly takes into account the record to record variation.•Simulation by random metamodel selection with no additional computational burden and distribution assumption.•Numerical elucidation of the effectiveness of the proposed approach for seismic reliability analysis. The present study deals with support vector regression-based metamodeling approach for efficient seismic reliability analysis of structure. Various metamodeling approaches e.g. response surface method, Kriging interpolation, artificial neural network, etc. are usually adopted to overcome computational challenge of simulation based seismic reliability analysis. However, the approximation capability of such empirical risk minimization principal-based metamodeling approach is largely affected by number of training samples. The support vector regression based on the principle of structural risk minimization has revealed improved response approximation ability using small sample learning. The approach is explored here for improved estimate of seismic reliability of structure in the framework of Monte Carlo Simulation technique. The parameters necessary to construct the metamodel are obtained by a simple effective search algorithm by solving an optimization sub-problem to minimize the mean square error obtained by cross-validation method. The simulation technique is readily applied by random selection of metamodel to implicitly consider record to record variations of earthquake. Without additional computational burden, the approach avoids a prior distribution assumption about approximated structural response unlike commonly used dual response surface method. The effectiveness of the proposed approach compared to the usual polynomial response surface and neural network based metamodels is numerically demonstrated.