Modified Genetic Algorithm for the Dynamic Identification of Structural Systems Using Incomplete Measurements

• Published in: Computer-aided civil and infrastructure engineering Vol. 26; no. 2; pp. 92 - 110
• Main Authors: Marano, Giuseppe Carlo, Quaranta, Giuseppe, Monti, Giorgio
• Format: Journal Article
• Language: English
• Published: Malden, USA Blackwell Publishing Inc 01.02.2011; Wiley
• Subjects: Applied sciences; Buildings. Public works; Computation methods. Tables. Charts; Dynamical systems; Dynamics; Exact sciences and technology; Genetic algorithms; Infrastructure; Stresses. Safety; Structural analysis. Stresses; Genetic algorithm; Mechanical system; Numerical simulation; Strategy; Identification; Stiffness; Measurement method; Application; Modeling; Comparative study; Structural analysis; Dynamic load
• ISSN: 1093-9687; 1467-8667
• DOI: 10.1111/j.1467-8667.2010.00659.x

:  A modified real‐coded genetic algorithm to identify the parameters of large structural systems subject to the dynamic loads is presented in this article. The proposed algorithm utilizes several subpopulations and a migration operator with a ring topology is periodically performed to allow the interaction between them. For each subpopulation, a specialized medley of recent genetic operators (crossover and mutation) has been adopted and is briefly discussed. The final algorithm includes a novel operator based on the auto‐adaptive asexual reproduction of the best individual in the current subpopulation. This latter is introduced to avoid a long stagnation at the start of the evolutionary process due to insufficient exploration as well as to attempt an improved local exploration around the current best solution at the end of the search. Moreover, a search space reduction technique is performed to improve, both convergence speed and final accuracy, allowing a genetic‐based search within a reduced region of the initial feasible domain. This numerical technique has been used to identify two shear‐type mechanical systems with 10 and 30 degrees‐of‐freedom, assuming as unknown parameters the mass, the stiffness, and the damping coefficients. The identification will be conducted starting from some noisy acceleration signals to verify, both the computational effectiveness and the accuracy of the proposed optimizer in presence of high noise‐to‐signal ratio. A critical and detailed analysis of the results is presented to investigate the inner work of the optimizer. Finally, its performances are examined and compared to the most recent results documented in the current literature to demonstrate the numerical competitiveness of the proposed strategy.