| Summary: | 碩士 === 國立交通大學 === 土木工程系所 === 106 === The method of structural parameter updating has been developed for decades. In most modal, frequency is the most known conditions for structural parameters updating. In this study, it is proposed that the efficiency of structural parameters’ modification and the stability of the solution are both taken into account in the case of partial modal data and incomplete measurement points. There are two stages in this method: the first stage is the stiffness revise index stage, and the second stage is the unsupervised fuzzy neural network of the elite strategy. The first stage will randomize the case in the global scope. During each iteration, it will calculate the respective stiffness index of each case, and each case is revised by the stiffness revise index to the target point. The difference between the solution and the target point can be around 10%, and there will not be too many iterations after the end of the first phase. Unsupervised fuzzy neural networks can quickly find accurate solutions at good initial conditions, so the second stage takes the results of the first stage output as the initial point of the unsupervised fuzzy neural network. The best solution will be the center in each iteration, and it will randomly distribute case in its vicinity. Elite strategy in each iteration of unsupervised fuzzy neural network keeps good examples from prior iteration for the sample of next iteration. This process can increase the weight at the first frequency to increase accuracy and stability of the solution. 6-story and 9-story shear-type structures are employed to verify the accuracy and stability of the proposed approach. The damage detection is based on the numerical model of the 6-story shear-type structures and the experimental model of the 3-story and 8-storey shear-type structures. The result revealed that the proposed approach can accurately and stably identify the damage locations by partial modal data.
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