In this study, firstly, the behavior of a high steel frame equipped with tuned mass damper (TMD) due to several seismic records is investigated considering the structural and seismic uncertainties. Then, machine learning methods including artificial neural networks (ANN), decision tree (DT), Naïve Bayes (NB) and support vector machines (SVM) are used to predict the behavior of the structure. Results showed that among the machine learning models, SVM with Gaussian kernel has better performance since it is capable of predicting the drift of stories and the failure probability with R2 value equal to 0.99. Furthermore, results of feature selection algorithms revealed that when using TMD in high steel structures, seismic uncertainties have greater influences on drift of stories in comparison with structural uncertainties. Findings of this study can be used in design and probabilistic analysis of high steel frames equipped with TMDs.
In this study, the probability of failure of steel frames with Tuned Mass Damper (TMD) is investigated. A 20-story steel frame was designed using SAP software and then, it was modelled in OpenSees. The behaviour of the frame towards accelerations in the range of 0.1 to the maximum acceleration of seismic records was investigated in this study. Using Incremental Dynamic Analysis (IDA) and extracting seismic fragility curves, it was observed that TMD increased the stability of the structure against the earthquakes by absorbing the input energy to the structure. This significantly reduces the possibility of failure due to small PGAs. For example, for the acceleration scheme equal to 0.35 for Tehran (the capital of Iran), the probability of failure in all stories of the structures equipped with TMD will be insignificant.
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