an hybrid real genetic algorithm to detect structural damage using modal properties

an hybrid real-coded genetic algorithm with damage penalization is implemented to locate and quantify structural damage. genetic algorithms provide a powerful tool to solved optimization problems. with an appropriate selection of their operators and parameters they can potentially explore the entire solution space and reach the global optimum. here, the set-up of the genetic algorithm operators and parameters is addressed, providing guidelines to their selection in similar damage detection problems. the performance of five fundamental functions based on modal data is studied. in addition, this paper proposes the use of a damage penalization that satisfactorily avoids false damage detection due to experimental noise or numerical errors. a tridimensional space frame structure with single and multiple damages scenarios provides an experimental framework which verifies the approach. the method is tested with different levels of incompleteness in the measured degrees of freedom. the results show that this approach reaches a much more precise solution than conventional optimization methods. a scenario of three simultaneous damage locations was correctly located and quantified by measuring only a 6.3% of the total degrees of freedom.