Evolutionary divide-and-conquer strategy for identification of structural systems and moving forces

Abstract

This study aims to develop a robust and efficient numerical strategy for identifying unknown parameters of large systems. The strategy is developed based on the excellent combination of two complementary merits, working on different principles, from divide-and-conquer approach and an evolutionary algorithm to significantly enhance the accuracy of identification results. While the former reduces the identification problem size, the latter focuses on the improvement of the search effectiveness. Therefore, this strategy is named evolutionary divide-and-conquer strategy. It works by dividing a large system with many unknowns into many smaller systems with manageable number of unknowns that are more accurately and efficiently identified by an improved genetic algorithm (GA). The first application of the proposed strategy focuses on identification for large structural systems. The large structures are sequentially decomposed into many smaller parts, called substructures. The robustness and effectiveness of the proposed strategy are illustrated on numerical simulation as well as experimental model tests of a 10-story steel structure. As an extension of the application, the proposed strategy is further developed for ?output-only? identification problems where the input excitation forces within the substructures of interest are immeasurable. In the content of structural health monitoring, the proposed strategy is applied for identifying damage in critical parts of large structures, commonly known as local structural damage quantification. In addition, in order to illustrate the versatility of the proposed strategy, moving force identification in time domain is studied. The proposed strategy identifies forces moving across a bridge by recursively breaking down the force time histories in a series of time sub-domains in which the initial displacement and force values are identified simultaneously.