Analysis of masonry panel under biaxial bending using ANNs and CBR

In this paper the capability of artificial neural networks (ANNs) in solvin g complex nonlinear problems is utilized for the analysis of masonry panels under biaxial bending. A network, trained using a set of data, which is re presentative of the problem domain, is shown to be successful in solving ne w problems with reasonable accuracy. The experimental results obtained from the testing of panels are analyzed using the existing theories, and the me thod 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 c ase-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 c ase library. In this paper a hybrid system is described that utilizes the c apabilities of both ANNs and CBR. CBR is used to identify a theoretical met hod 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 desi gn of masonry panels subjected to biaxial bending.