Automatic calibration of a discrete element model of a masonry arch by swarm intelligence methods

Abstract

This paper addresses the automatic calibration of constitutive parameters of the Discrete Element Method (DEM) models of masonry structures. Three swarm intelligence algorithms were applied, which are efficient in problems characterised by the small number of unknown parameters but high computational demand for the evaluation of the target function. Trust-Based Particle Swarm Optimisation (TBPSO) was chosen due to its excellent convergence rate. It was already tested that TBPSO converged faster than the original Genetic Algorithm and Particle Swarm Optimisation Methods. Therefore, TBPSO was compared with two recent and promising methods, the Marine Predator Algorithm (MPA) and the Golden Eagle Optimiser (GEO). The paper also presents a novel two-step approach, which enhances the performance of the tested optimisation strategies. The methodology is capable to account for and precisely find the mode of failure using Convolutional Neural Networks (CNN). The three optimisation methods and the two-step approach were applied on the DEM model of a quasi-static masonry arch collapse experiment from the literature. Performance of the three algorithms in identifying the appropriate constitutive parameters values of the DEM model were compared. Clear evidence on the advantage of the two-step approach with TBPSO is demonstrated. Remarks about different modelling issues are also included.