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

Abstract

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

3D displacement measurement model for health monitoring of structures using a motion capture system

“…Accurate assessment of safety, however, is difficult, due to the analytical limitation and the fact that numerous assumptions must be made in the assessment process, considering the inherent uncertainties of large and complex civil structures. In this respect, in recent years many researchers have developed parametric system identification methods to estimate the structural parameters or non-parametric methods to estimate the structural response directly to be used in the analysis of the behavior of complex structures and determine their state of damage (Jiang and Adeli 2005;Adeli and Jiang 2006;Schoefs et al 2011;Marano et al 2011;Jafarkhani and Masri 2011;Sirca and Adeli 2012;Kang et al 2012;Hazra 2012;Yan and Ren 2012;Cho et al 2012;Fuggini et al 2013;Su et al, 2014). In addition, serviceability assessments using acceleration data require analysis of frequency and amplitude after measurements are completed making it difficult for the engineer to make a decision intuitively at the time of measurement.…”

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“…Jung et al (2010) used static measurements as damage index and multiobjective optimization as a search method for a 2-D simple truss structure. Marano et al (2011) proposed a modified single-objective GA to identify a 2-D structural system subjected to dynamic loads. Fuggini et al (2013) carried out system identification of retrofitting textile using inverse problem solving based on GAs.…”

New Tool to Help Decision Making in Civil Engineering

“…This example proves that even regular inspections cannot guarantee structural Downloaded by [Univ Politec Cat], [Jose TURMO] at 00:39 15 June 2015 have any explicit physical meaning, which is their main drawback. During the last decades, computer development has enabled increasing popularity in non-parametric methods such as Neural Networks (Talebinejad et al 2011) or Genetic Algorithms (Marano et al 2011), as complex structural systems can be identified, whereas parametric methods have mainly been applied to very simple models, due to the complexity of the mathematical relations among the variables.…”