Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0045-7949(02)00344-9
Title: A hybrid computational strategy for identification of structural parameters
Authors: Koh, C.G. 
Chen, Y.F.
Liaw, C.-Y. 
Keywords: Computational method
Genetic algorithms
Local search
Structural identification
Issue Date: Feb-2003
Source: Koh, C.G.,Chen, Y.F.,Liaw, C.-Y. (2003-02). A hybrid computational strategy for identification of structural parameters. Computers and Structures 81 (2) : 107-117. ScholarBank@NUS Repository. https://doi.org/10.1016/S0045-7949(02)00344-9
Abstract: 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. © 2002 Elsevier Science Ltd. All rights reserved.
Source Title: Computers and Structures
URI: http://scholarbank.nus.edu.sg/handle/10635/54261
ISSN: 00457949
DOI: 10.1016/S0045-7949(02)00344-9
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