A hybrid computational strategy for identification of structural parameters

By identifying parameters such as stiffness values of a structural system, the numerical model can be updated to give more accurate response prediction or to monitor the state of the structure. Considerable progress has been made in this subject area, but most research works have considered only small systems. A major challenge lies in obtaining good identification results for systems with many unknown parameters. In this study, a non-classical approach is adopted involving the use of genetic algorithms (GA). Nevertheless, direct application of GA does not necessarily work, particularly with regards to computational efficiency in fine-tuning when the solution approaches the optimal value. A hybrid computational strategy is thus proposed, combining GA with a compatible local search operator. Two hybrid methods are formulated and illustrated by numerical simulation studies to perform significantly better than the GA method without local search. A fairly large structural system with 52 unknown parameters is identified with good results, taking into consideration the effects of incomplete measurement and noisy data.