Substructural and progressive structural identification methods

Abstract

While it is possible in principle to determine unknown structural parameters by system identification techniques, a major challenge lies in the numerical difficulty in obtaining reasonably accurate results when the system size is large. Adopting the strategy of “divide-and-conquer” to address this issue, substructural identification and progressive structural identification methods are formulated. The main idea is to divide the structure into substructures such that the number of unknown parameters is within manageable size in each stage of identification. A non-classical approach of genetic algorithms is employed as the search tool for its several advantages including ease of implementation and desirable characteristics of global search. Numerical simulation study is presented, including a fairly large system of 50 degrees of freedom, to illustrate the identification accuracy and efficiency. The methods are tested for known-mass and unknown-mass systems with up to 102 unknown parameters, accounting for the effects of incomplete and noisy measurements.