: 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.

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

A hybrid computational strategy for identification of structural parameters

Substructural and progressive structural identification methods

Differential evolution strategy for structural system identification

Substructural identification provides a novel means by which to reduce a large problem to smaller problems of manageable size, thereby improving numerical convergence and accuracy. Various methods proposed by several researchers thus far require interface response measurements, which are then treated as input to the substructures of concern. In practice, however, it is not always possible to obtain interface measurements, particularly if rotational response is required for beam/frame structures. In this paper, a method for parameter identification of substructures without the need of interface measurements is proposed. On the basis of receptance theory, an inverse problem is formulated in the frequency domain. Interface forces are eliminated by using different sets of measurements in the substructure concerned under the same dynamic excitation. The genetic algorithms approach is employed to determine the unknown parameters, and the fitness function is defined to minimize the difference between the estimates of interface forces obtained using different sets of response measurements. Three numerical examples are presented to illustrate the proposed method, and account for effects of measurement noise.

Substructural Identification Method without Interface Measurement

Though many methods of system identification are currently available for parameter estimation of structural systems, the challenge lies in the numerical difficulty in convergence when the number of unknowns is large. In this study, the genetic algorithms (GA) approach is adopted, which has several advantages over classical system identification techniques. Nevertheless, if applied directly, this approach requires tremendous computational time when dealing with structural systems large in both unknowns and degrees of freedom. A method is proposed herein to alleviate this problem by conducting a GA search in modal domains of a much smaller dimension than the physical domain. The objective function is defined based on the estimated modal response in time domain and the corresponding modal response transformed from the measured response. With some modification, this method also works well even with incomplete response measurement. Numerical examples of structural systems of up to 50 degrees of freedom are presented. Effects of measurement noise are considered.

B. Hong

Papers

Parameter identification of large structural systems in time domain