Abstract
Use of composite steel shear walls (CSSW) in earthquake-resistant structures has grown in recent years. However, no thorough information exists on their performance, especially in cases where openings are present. In the present study, in order to first validate the analysis method, ABAQUS was used to model the studied composite shear wall with gap at UC-Berkeley, according to the results of which, a good agreement between the experimental and analytical models was observed. Then, the effect of the position and number of the openings on the performance of the walls was addressed. To this end, models with various openings, including openings close to the beam/column, horizontal/vertical openings and distributing opening, were prepared and analyzed. The results indicate that the maximum reduction in stiffness and strength occurred in walls with single openings. The size of opening and the opening’s area significantly affect shear wall performance. Ultimately, artificial neural network and fitness function tools were employed to obtain predictive models for shear wall performance. A neural network has proven an appropriate alternative method for predicting the displacement, stress, and strength of the composite shear wall.
Highlights
Reinforced concrete shear walls are amongst the most efficient seismic resistant systems in structures, which have been manufactured using metallic materials in recent years
For after researchers, compiling the neural was used to predict the wall; few pieces of research have been conducted to find out the effect of an opening is finite element method (FEM) result and the influence of the opening size and opening location on the shearthat wall necessary for the composite steel shear wall.In this paper, firstly, the validation of the analysis method performance was obtained
For the composite steel wall studied in this research, artificial neural network (ANN) was used to obtain predictive models
Summary
Reinforced concrete shear walls are amongst the most efficient seismic resistant systems in structures, which have been manufactured using metallic materials in recent years. For after researchers, compiling the neural was used to predict the wall; few pieces of research have been conducted to find out the effect of an opening is FEM result and the influence of the opening size and opening location on the shearthat wall necessary for the composite steel shear wall.In this paper, firstly, the validation of the analysis method performance was obtained. The concrete wall is in contact with steel frame, experimental laboratory test was introduced and the result of ABAQUS was compared with the while there is a gap in the innovation model (Astana [17]). Experimental set-up;from the finite element software and the experimental results are shown and compared in (b) ABAQUS model; (c) steel shear plate after testing; (d) concrete plate.
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