Modeling uncertainty propagation in Eccentric Braced Frames using Endurance Time method and Radial Basis Function networks

Abstract

A robust analytical model of Eccentric Braced Frames (EBFs), as a well-known seismic resistance system, helps to comprehensive earthquake-induced risk assessment of buildings in different performance levels. Recently, the modeling parameters have been introduced to simulate the hysteretic behavior of shear links in EBFs with specific Coefficient of Variation associated with each parameter to consider the uncertainties. The main purpose of this paper is to assess the effect of these uncertainties in the seismic response of EBFs by combining different sources of aleatory and epistemic uncertainties while making a balance between the required computational effort and the accuracy of the responses. This assessment is carried out in multiple performance levels using Endurance Time (ET) method as an efficient Nonlinear Time History Analysis. To demonstrate the method, a 4-story EBF that considers behavioral parameters has been considered. First, a sensitivity analysis using One-Variable-At-a-Time procedure and the ET method has been utilized to sort the parameters with regard to their importance in seismic responses in two intensity levels. A sampling-based reliability method is first used to propagate the modeling uncertainties into the fragility curves of the structure. Radial Basis Function Networks are then utilized to estimate the structural responses, which makes it feasible to propagate the uncertainties with an affordable computational effort. The Design of Experiments technique is implemented to acquire the training data, reducing the required data. The results show that the mathematical relationships defined by Artificial Neural Networks and using the ET method can estimate the median Intensity Measures and shifts in dispersions with acceptable accuracy.

Graphical abstract

Keywords

Eccentric Braced Frames / uncertainty propagation / behavioral parameters / Endurance Time method / correlation Latin hypercube sampling / Artificial Neural Networks / Radial Basis Function networks

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Acknowledgements

The authors would like to express their gratitude to Dr. Kiarash M. Dolatshahi and Mr. Omid Moammer from Sharif University of Technology for providing additional data on modeling parameters of shear links.

Competing interests

The authors declare that they have no competing interest.

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