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Suggestion of an equation of motion to calculate the damping ratio during earthquake based on a cyclic procedure

Naderpour H., Barros R. C., Khatami S. M.

Journal of Theoretical and Applied Mechanics

2016

Vol. 54 nr 3

963--973

Large horizontal relative displacements are naturally caused by seismic excitation, which is able to provide collisions between two adjacent buildings due to insufficient separation distance and severe damages due to impacts, especially in tall buildings. In this paper, the impact is numerically simulated and two needed parameters are calculated, including the impact force and energy absorption. In order to calculate the mentioned parameters, mathematical study is carried out to model an unreal link element, which is logically assumed to be a spring and dashpot to determine the lateral displacement and damping ratio of the impact. For the determination of the dynamic response of the impact, a new equation of motion is theoretically suggested to the evaluate impact force and energy dissipation. In order to confirm the rendered equation, a series of parametric studies are performed and the accuracy of the formulas is confirmed.

Utilization of artificial neural networks to prediction of the capacity of CCFT short columns subject to short term axial load

Ahmadi M., Naderpour H., Kheyroddin A.

Archives of Civil and Mechanical Engineering

2014

Vol. 14, no. 3

510--517

Due to high strength and ductility, concrete filled steel tube columns have been highly regarded in recent decades and many experimental studies have been carried out in predicting the strength of these columns. Increase in compressive strength of concrete core by the lateral confinement provided by steel tube and delay of the steel local buckling by the contact with the hardened concrete are effective parameters in behavior of concrete filled steel tubes. This study presents a new approach to predict the capacity of circular concrete filled steel tube columns under axial loading condition, using a large number of experimental data by applying artificial neural networks. The effects of yield stress and wall thickness of steel tube, compressive strength of concrete and dimensions of column are examined. Proposed equation is compared with other existing models and indicates that the new model can predict the ultimate strength of axially loaded columns by a high level of precision.