Publication Date:
2019
abstract:
The main objectives of Structural Health Monitoring (SHM) are the characterization and the
assessment of the health condition of structural systems. Combined with appropriate Damage Identification
(DI) strategies, SHM aims to provide reliable information about the localization and quantification of the
structural damage by using an inverse formulation approach, with the damage parameters being estimated from
parametric changes in dynamic properties. Mathematically, an inverse problem consists of the optimization of
a function which represents the “distance” between the experimental and the numerically-simulated features of
the system. Such process requires the development of a mock-up numerical model fairly representative of the
system and iteratively updated until a response, as close as possible to the experimental one, is provided. The
minimization of the difference between measured and predicted features’ values is the objective function, whose
global minimum corresponds to the best adjustment of the model variables. Metaheuristics represent a large class
of global methods for optimization purposes able to outperform traditional methods in the following aspects: ease
of implementation, time consumption, suitability for non-linear phenomena, black-box and high-dimensional
problems. The present paper analyses, through a numerical experimentation approach, the suitability of one
of the best-known metaheuristics, i.e. the Particle Swarm Optimization (PSO) algorithm, for DI of beam-like
structures. Modal properties are used to define the objective function and various algorithm instances are tested
across different problem instances to assess robustness and influence of the algorithm parameters
assessment of the health condition of structural systems. Combined with appropriate Damage Identification
(DI) strategies, SHM aims to provide reliable information about the localization and quantification of the
structural damage by using an inverse formulation approach, with the damage parameters being estimated from
parametric changes in dynamic properties. Mathematically, an inverse problem consists of the optimization of
a function which represents the “distance” between the experimental and the numerically-simulated features of
the system. Such process requires the development of a mock-up numerical model fairly representative of the
system and iteratively updated until a response, as close as possible to the experimental one, is provided. The
minimization of the difference between measured and predicted features’ values is the objective function, whose
global minimum corresponds to the best adjustment of the model variables. Metaheuristics represent a large class
of global methods for optimization purposes able to outperform traditional methods in the following aspects: ease
of implementation, time consumption, suitability for non-linear phenomena, black-box and high-dimensional
problems. The present paper analyses, through a numerical experimentation approach, the suitability of one
of the best-known metaheuristics, i.e. the Particle Swarm Optimization (PSO) algorithm, for DI of beam-like
structures. Modal properties are used to define the objective function and various algorithm instances are tested
across different problem instances to assess robustness and influence of the algorithm parameters
Iris type:
2.1 Contributo in volume (Capitolo o Saggio)
List of contributors:
Barontini, A; Masciotta, Mg; Ramos, Lf; Lourenco, Pb; Amado-Mendes, P
Book title:
Life-Cycle Analysis and Assessment in Civil Engineering: Towards an Integrated Vision - Proceedings of the 6th International Symposium on Life-Cycle Civil Engineering (IALCCE 2018)