Numerical analysis of large masonry structures: bridging meso and macro scales via artificial neural networks.

• A methodology for analysis of large-scale masonry structures is presented which incorporates an inelastic macroscopic framework coupled with a series of artificial neural networks. • The neural networks serve the purpose of identification of material functions embedded in anisotropic formulation as well as the specification of localization plane. • The data required for training of neural networks is generated using 'virtual experiments' that involve a mesoscale finite element analysis of masonry wallets. • Finite element analysis of a large masonry wall with multiple openings is carried out. The results are compared with those based on a detailed mesoscale model. This paper presents a methodology for analysis of large-scale masonry structures. The approach involves development of a series of artificial neural networks which enable the identification of main variables employed in the macroscopic formulation that incorporates an inelastic constitutive law with embedded discontinuity. The data required for training of neural networks is generated using 'virtual experiments', whereby the 'equivalent' anisotropic response of masonry is obtained through a mesoscale finite element analysis of masonry wallets. The paper outlines the procedure for identification of approximation coefficients describing the orientation-dependency of strength, and other relevant parameters. A numerical example is provided involving analysis of a large masonry wall with multiple openings. The results of macroscale approach are compared with those based on a detailed mesoscale model for the same geometry and boundary conditions. [ABSTRACT FROM AUTHOR]