Analysis of Masonry Panel under Biaxial Bending Using ANNs and CBR

Abstract

In this paper the capability of artificial neural networks (ANNs) in solving complex nonlinear problems is utilized for the analysis of masonry panels under biaxial bending. A network, trained using a set of data, which is representative of the problem domain, is shown to be successful in solving new problems with reasonable accuracy. The experimental results obtained from the testing of panels are analyzed using the existing theories, and the method that gives good correlation between the theoretical prediction and the experimental result is recommended for other panels of similar properties and boundary conditions. An artificial intelligence based technology, the case-based reasoning (CBR), has been used to solve new problems by adapting solutions to similar problems solved in the past, which are stored in the case library. In this paper a hybrid system is described that utilizes the capabilities of both ANNs and CBR. CBR is used to identify a theoretical method that is most suitable for the present problem, whereas ANNs are used to arrive at a solution with great savings in computational time for the design of masonry panels subjected to biaxial bending.