| Summary: | 碩士 === 朝陽科技大學 === 營建工程系碩士班 === 92 === Located at the active arc-continent collision region between the Luzon arc of the Philippine Sea plate and the Eurasian plate, Taiwan is subject to frequent earthquakes. This continual seismic activity which caused great damages to structures resulted in more emphases on the earthquake resistant design of buildings. Structure properties may be deteriorated and degraded with time in an unexpected way due to randomness in the environment and loadings over its lifetime. In particular, when a structure is exposed to strong earthquake, the properties of the structure may be changed and its behavior after an earthquake can be different from that before the earthquake. In order to realize the dynamic behavior of structural systems, we can determine the dynamic models and parameters by system identification techniques.
In this study, the single layer neural network is first employed to identify the system parameters of both the SDOF system and the MDOF system. There are two kinds of earthquakes to be used as the system input, one is the artificial earthquake, and the other is the real earthquake. The associated system response is then calculated assuming the system parameters. In addition to the traditional batch mode algorithm, the improved batch algorithm is also used as the training algorithm, while performing the single layer neural network to the above identification problems. The validity and the efficiency of the proposed algorithms are explored by comparing the results of the predicted response with the measured response for both the SDOF system and the MDOF system with or without noise contamination.
Furthermore, the advantages of the genetic algorithm and the above single layer neural network are combined to yield a new identification technique. The network topology is employed to replace the procedure for solving the governing (differential) equation when GA is used to identify the system parameters of both the SDOF system and the MDOF system with or without noise contamination. The comparison is made between the predicted acceleration and the measured one for each case.
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