Combining Genetic Algorithms with a Meso-Scale Approach for System Identification of a Smart Polymeric Textile

This article describes a structural system identification approach for the characterization of a novel retrofitting textile, the Composite Seismic Wallpaper. This polymeric textile was developed within the EU co-funded project Polytect as a full coverage method for increasing the seismic resistance of masonry structures. Recently, the wallpaper has been full-scale tested, on a two storey building, at the Eucentre (Pavia) as part of the Seismic Engineering Research Infrastructures for European Synergies (SERIES) program. In this article, an advanced multistage identification methodology is proposed for the successful simulation of this novel material based on the results of the extensive experimental campaign. The identification is essentially formulated as an inverse problem that combines a Genetic Algorithm (GA) as the optimizer and a finite element (FE) model as the physical model of the structure. The aim is material characterization and modeling of the dynamic response of the structure; an issue which is nontrivial due to the intrinsic complexities associated with both masonry and polymers. The process outlined herein is successful in yielding a calibrated model that can more accurately capture the experimentally observed behavior of this three-dimensional full-scale test case.