Please use this identifier to cite or link to this item: https://doi.org/10.1117/12.434171
Title: Hybrid global-local identification for structural health monitoring
Authors: Koh, C.G. 
Liaw, C.Y. 
Chen, Y.F.
Keywords: Genetic algorithms
Local search
Structural health monitoring
System identification
Issue Date: 2001
Source: Koh, C.G., Liaw, C.Y., Chen, Y.F. (2001). Hybrid global-local identification for structural health monitoring. Proceedings of SPIE - The International Society for Optical Engineering 4335 : 174-179. ScholarBank@NUS Repository. https://doi.org/10.1117/12.434171
Abstract: Genetic algorithms (GA) have been widely used in many optimization and system identification problems. Nevertheless, largely because of the stochastic nature, the approach of using GA has been found to be not efficient in "fine-tuning" in terms of convergence to the optimal solution from its neighborhood. In this study, the GA approach as a global search tool is combined with a local search (LS) method which is developed on the basis of the classical univariate method, so as to expedite the search process by perturbations near the optimal solution. This LS method, herein named the modified univariate method, does not search all the unknown variables of the problem but only a few selected ones. Furthermore, the initial step size of LS is adjusted according to the average efficiency of each LS operator. For comparison, the Solis-and-Wets LS method is also considered. A numerical example of 10-DOF structure is presented to compare the accuracy and efficiency of the various methods considered.
Source Title: Proceedings of SPIE - The International Society for Optical Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/74198
ISSN: 0277786X
DOI: 10.1117/12.434171
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