Abstract
There is a large amount of useful information from past experimental tests, which are usually ignored in test-setup for the new ones. Variation of assumptions, materials, test procedures, and test objectives make it difficult to choose the right model for validation of the numerical models. Results from different experiments are sometimes in conflict with each other, or have minimum correlation. Furthermore, not all these information are easily accessible for researchers and engineers. Therefore, this paper presents the results of a comprehensive study on different experimental models for steel plate and reinforced concrete shear walls. A unique library of up to 13 parameters (mechanical properties and geometric characteristics) affecting the strength, stiffness and drift ratio of the shear walls are gathered including their sensitivity analysis. Next, a predictive meta-model is developed based on artificial neural network. It is capable of forecasting the responses for any desired shear wall with good accuracy. The proposed network can be used to as an alternative to the nonlinear numerical simulations or expensive experimental test.
Highlights
Bertero [1] defined the shear wall (SW) as a very stiff member, with high resistance to deformation under load
The SW system can be classified as reinforced concrete shear walls (RCSW) [2,3,4], steel plate shear wall (SPSW) [5,6], masonry shear walls [7,8,9], composite shear walls, and timber plate shear walls [10,11]
The parameters were mainly related to the geometry of the shear walls, as well as the applied loads, and the material properties
Summary
Bertero [1] defined the shear wall (SW) as a very stiff member, with high resistance to deformation under load. The authors have been confronted by such a problems in their previous studies on both steel and RC shear walls from experimental [15] and numerical [19] points of view This was one of the motivations to develop such a comprehensive database of SW models, which would be useful for all the future studies related to the experimental and numerical validations. On the other hand, such a huge database might be difficult to track by practitioners, and, a meta-model is required to summarize and present the results in a systematic way. This should include the parameters affecting the stiffness and strength of the SWs, as well as their sensitivity. It can predict other results that have not been investigated before
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