Abstract. Nowadays, using high-ductility structures in the construction and use of significant buildings is highly appreciated. To use more ductile structures, effort has been made in this research to introduce box-plate beam-to-column connections. They underwent hysteretic loading and it was found from their moment-rotation curves that the bending capacity and ductility of the box-plate connection were more than ordinary rigid connection, and those of the latter were more than those of the normal typical one. It was also shown that stress concentration in box-plate connections disappears over the top and bottom flange plates.
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.
This article proposes a comprehensive closed-form approach to determine the dynamic response of single-degree-of-freedom and multi-degree-of-freedom systems subjected to stationary and non-stationary Gaussian excitations. The proposed method presents the probabilistic seismic response of adjacent systems under different levels of seismic intensity, in terms of summarized incremental dynamic analysis curves, and has the ability to consider the seismic pounding effect based on the standard deviation of the response of adjacent systems. In comparison with the incremental dynamic analysis procedure, the proposed approach shows acceptable results with much less computational time and cost.
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