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Showing 3 results for Nonlinear Time History Analysis

R. Kamyab, E. Salajegheh,
Volume 1, Issue 3 (9-2011)

This study deals with predicting nonlinear time history deflection of scallop domes subject to earthquake loading employing neural network technique. Scallop domes have alternate ridged and grooves that radiate from the centre. There are two main types of scallop domes, lattice and continuous, which the latticed type of scallop domes is considered in the present paper. Due to the large number of the structural nodes and elements of scallop domes, nonlinear time history analysis of such structures is time consuming. In this study to reduce the computational burden radial basis function (RBF) neural network is utilized. The type of inputs of neural network models seriously affects the computational performance and accuracy of the network. Two types of input vectors: cross-sectional properties and natural periods of the structures can be employed for neural network training. In this paper the most influential natural periods of the structure are determined by adaptive neuro-fuzzy inference system (ANFIS) and then are used as the input vector of the RBF network. Results of illustrative example demonstrate high performance and computational accuracy of RBF network.
M. Jamshidi Avanaki , H.e. Estekanchi,
Volume 2, Issue 2 (6-2012)

Estimation of collapse performance is primarily conducted through Collapse Fragility Curves (CFC’s). The EDP-based approach is the main scheme for attaining such curves and employs IDA. Obtaining CFC’s from IDA results is tremendously time consuming and computationally demanding. Introduction of more efficient methods of seismic analysis, can potentially improve this issue. The Endurance Time (ET) method is a straightforward method for dynamic analysis of structures subjected to multilevel excitation intensities. In this paper, collapse analysis using ET analysis results to obtain EDP-based CFC’s, has been explained and demonstrated by a model. For verification, the resulting CFC has been compared to that obtained by IDA.
K. Shakeri,
Volume 3, Issue 2 (6-2013)

In recent years some multi-mode pushover procedures taking into account higher mode effects, have been proposed. The responses of considered modes are combined by the quadratic combination rules, while using the elastic modal combination rules in the inelastic phases is not valid. Here, an optimum weighted mode combination method for nonlinear static analysis is presented. Genetic algorithm is used for optimization of the modal weight. The proposed procedure is applied for a sample building. The results show that the resulted response from the proposed method has minimal error in comparison with the response of the nonlinear time history analysis.

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