Damage Localization in Framed Structures using Peridynamic Models

This study investigates a novel technique to estimate new local vibration parameters to monitor existing buildings in the structural health monitoring field. The paper also gives a comprehensive discussion on their use for damage localization, as well as how these parameters are related to the variations of structural eigen-properties, with the aim of connecting local with global damage features. In order to estimate these new local vibrational parameters, the authors propose the implementation of a new modeling strategy based on peridynamics, which represents a novel continuum theory. This is performed under operational conditions assumptions, i.e., without the need to force the system with external inputs. The idea is to exploit the discrete formulation of peridynamics to build low-fidelity structural models, useful for rapid and scalable analyses. The focus of this study is to generate a network of points and bonds, where each point represents the spatial position of a significant location within a structure. The goal is to construct a bond force network that structurally connects these points, approximating the structural topology and connectivity of the system. Starting from the spatial coordinates of these points, the authors apply the peridynamic theory to determine the corresponding values of mass, stiffness, and damping parameters. This allows to build a simplified yet physically meaningful model of the structure, suitable for structural health monitoring. Within this framework, two parameters are obtained to localize damage: Bond Extremity Acceleration (BEA) and Bond Extremity Velocity (BEV). Both are derived from a micro-viscoelastic formulation of peridynamic bonds and can be indirectly estimated from acceleration response data using established techniques. The methodology is tested on a numerical reproduction of a three-story aluminum frame, modeled with 36 degrees of freedom and subjected to Gaussian noise. The conclusions of the study suggest that low-fidelity peridynamic models can be a useful basis for developing physical representations of structural networks, suitable for monitoring existing buildings and infrastructures.