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Stochastic response of reinforced concrete buildings using high dimensional model representation

•Evaluation of seismic response using high dimensional model representation.•Less sampling points compared to traditional statistical approaches.•Generation of fragility curves of concrete frames using high dimensional model representation.•Meta-model based fragility analysis. Dynamic responses of s...

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Bibliographic Details
Published in:Engineering structures 2019-01, Vol.179, p.412-422
Main Authors: Sahu, Deepak, Nishanth, M., Dhir, Prateek Kumar, Sarkar, Pradip, Davis, Robin, Mangalathu, Sujith
Format: Article
Language:English
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Summary:•Evaluation of seismic response using high dimensional model representation.•Less sampling points compared to traditional statistical approaches.•Generation of fragility curves of concrete frames using high dimensional model representation.•Meta-model based fragility analysis. Dynamic responses of structures are random in nature due to the uncertainties in geometry, material properties, and loading. The random dynamic responses can be represented fairly well by stochastic analysis. The methods used for stochastic analysis can be grouped into statistical and non-statistical approaches. Although statistical approaches like Monte Carlo simulation is considered as an accurate method for the stochastic analysis, computationally less intensive yet efficient, simplified non-statistical methods are necessary as an alternative. The present study is an evaluation of a relatively new non-statistical metamodel-based approach known as, High Dimensional Model Representation, with reference to existing response surface methods such as Central Composite Design, Box Behnken Design, and Full Factorial Design, in a dynamic response analysis. The geometry of a reinforced concrete frame is chosen to conduct free vibration and nonlinear dynamic analysis to study the stochastic responses using High Dimensional Model Representation method. This method was found to provide results as good as other methods with less computational effort with regard to the selected case studies.
ISSN:0141-0296
1873-7323
DOI:10.1016/j.engstruct.2018.10.083