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- W2053793082 abstract "A reliable computational model is necessary for evaluating the state and predicting the future performance of existing structures, especially after exposure to damaging effects such as an earthquake. A major problem with the existing iterative-based model updating methods is that the search might be trapped in local optima. The genetic algorithms (GAs) offer a desirable alternative because of their ability in performing a robust search for the global optimal solution. This paper presents a GA-based model updating approach using a real-coding scheme for global model updating based on dynamic measurement data. An eigensensitivity method is employed to further fine-tune the GA updated results in case the sensitivity problem arises due to restricted measurement information. The application on shear-type frames reveals that with a limited amount of modal data, namely the lowest three natural frequencies and the first mode shape, it is possible to achieve satisfactory updating by the GA alone for cases involving a limited number of parameters (storey stiffness herein). With the incorporation of the eigensensitivity algorithm, the updating capability is extended to a sufficiently large number of parameters. In case the modal data contain errors, the GA is also shown to be able to update the model to a satisfactory accuracy, provided the required amount of modal data is available. An example is given in which a 6-DOF stick model for an actual six-storey RC frame is updated using the measured dynamic properties. The effectiveness of the updating is evaluated by comparing the measured and predicted seismic response using the updated model. Copyright © 2005 John Wiley & Sons, Ltd." @default.
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- W2053793082 date "2005-01-01" @default.
- W2053793082 modified "2023-09-24" @default.
- W2053793082 title "Dynamic model updating using a combined genetic-eigensensitivity algorithm and application in seismic response prediction" @default.
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- W2053793082 doi "https://doi.org/10.1002/eqe.472" @default.
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