摘要
結構系統遭受地震作用後，可藉由系統識別方式觀察系統特性的變化，例如樓層側向勁度與偏心距的識別結果，以判別損壞程度與位置。本研究以時間域的非線性迴歸分析方法識別扭轉耦合系統的樓層參數，在完全量測且雜訊介入的情況下，樓層側向勁度於強動階段有較佳的識別結果，能反映出樓層損壞的程度。非線性迴歸分析需要給予識別參數的初始值，本研究以系統的運動方程式求解參數初始值，若將數值積分的反應訊號經由小波轉換濾波，可降低雜訊的干擾，取得較佳的參數初始值。本文的識別步驟經由模擬系統的含雜訊反應和標竿結構的實測紀錄驗證，具有不錯的樓層側向勁度識別效果。

Abstract
When a structural system is attacked by an earthquake, the variations of dynamic parameters, for example, the story lateral stiffness and the floor eccentric distance, could be identified in order to assess the damage severity and location. A nonlinear regression analysis in the time domain is performed in this study to identify the story parameters of torsion-coupled systems. Even though noises are added, the identified story lateral stiffness in strong-motion state is good enough to observe the damage severity for cases of full measurements. Initial guesses are required in performing the nonlinear regression analysis. These initial values of parameters are found here by using the equations of motion. If the response signals, which are numerically integrated through accelerograms with noises, are filtered with the help of wavelet transform and then are used in the equations of motion, then the calculated parameters would be better initial guesses in the following identification. Such a procedure gives very good results parametric of story lateral stiffness, which are validated by using both the responses of simulating systems and the records of several benchmark models under different base excitations.

參考文獻
[1]秦前清，楊宗凱，「實用小波分析」，西安電子科技大學出版社，1994。
[2]洪意晴，「平面不對稱建築物樓層勁度與剛心位置之識別」，國立成功
大學土木工程研究所碩士論文，台南，2008。
[3]黃李暉，「建築物樓層勁度識別與損壞評估之研究」，國立成功大學土
木工程研究所碩士論文，台南，2006。
[4]林岑彥，「應用小波轉換於結構物之系統識別」，國立交通大學土木工
程研究所碩士論文，新竹，2003。
[5]陳銘逵，「小波解析法之計算及其應用」，國立成功大學化學工程研究
所博士論文，台南，1995。
[6]蔡展榮，「小波基礎理論與應用」，授課講義，國立成功大學，台南，2008。
[7]Burrus, C. S., Gopinath, R. A, and Guo, H, “Introduction to Wavelets and Wavelet Transforms: A Primer”, Pearson Education Asia Limited and China Machine, Beijing, 2005.
[8]Chui, C. K., “An Introduction to Wavelets”, Academic Press, Boston, 1992.
[9]Dauberchies, I., “Orthonormal bases of compactly supported wavelets” , Comm. Pure Appl. Math., Vol. 41, pp. 909-996, 1988.
[10]Mallat, S. G., “A theory for multiresolution approximation signal decomposition: the wavelet representation”, IEEE Trans. PRMI, Vol. 11, pp. 674-693, 1989.