| 姓名 | 林東廣(Dong-Guang Lin) | 電子郵件信箱 | E-mail 資料不公開 | |
| 畢業系所 | 營建工程系碩士班(Department and Graduate Institute of Constrction Engineering) | |||
| 畢業學位 | 碩士(Master) | 畢業時期 | 95學年第2學期 | |
| 論文名稱(中) | 應用基因演算法為基礎之推廣卡氏過濾理論於結構系統識別 | |||
| 論文名稱(英) | Structural System Identification Using GA Based Extended Kalman Filter | |||
| 檔案 |
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。 |
論文使用權限 | 校內外完全公開 | |
| 論文語文/頁數 | 中文/166 | |||
| 摘要(中) |
在各種系統識別方法發展下，地震紀錄的取得是必需的。目前台灣地區在各地重要之大樓、學校及橋樑等土木結構物上皆裝有強震儀，可隨時紀錄地震發生時之相關資料，利用其地震紀錄來識別對應之結構系統參數，據此進行結構物安全評估，並根據地震後結構物的破壞情形，進行修補工作。所發展之系統識別方法，及其數學模式架構，可以應用於往後類似建築物之系統識別上，便於快速了解該建築物受震後之結構系統特性。
本研究所提出的新式識別法是以基因演算法為基礎之推廣卡氏過濾理論，它是在每進行一次基因演算世代循環中，搜尋及演化推廣卡氏過濾理論識別時所需之起始值；相對的，也藉由推廣卡氏過濾理論快速識別系統參數，增快基因演算法的收斂率，大幅減少運算時間。首先將新式識別法應用於數值模擬系統之動力特性識別，驗証其可行性；也為了更加確定以基因算法為基礎之推廣卡氏過濾理論之可行性，亦對於含有雜訊的數值模擬地震紀錄及反應再進行識別來探討新式識別法之識別效果。最後再應用新式識別法於真實大樓-台電大樓及中興大學土木環工大樓進行系統識別，採用新式識別法應用於單向擾動系統、雙向擾動系統以及單一輸入多重輸出系統之識別上，接著將其所識別的振態頻率與富氏振幅圖顯示之結果，來說明應用以基因演算法為基礎之推廣卡氏過濾理論於真實大樓之可行性。 |
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| 摘要(英) |
Field of system identification has become important discipline
due to the increasing need to estimate the behavior of a system
with partially known dynamics. In the past few decades, many
optimization techniques have been developed for system
identification problems. Identification is basically a process
of developing or improving a mathematical model of a dynamic
system through the use of measured experimental data. However,
collecting of strong motion data is essential when performing
the system identification analysis. Fortunately, the strong
motion data recorded by accelerographs, which were installed
under the Taiwan Strong-Motion Instrumented Program (TSMIP)
since 1993, has accumulated to a remarkable amount. In addition
to updating the structural parameters for better response
prediction, system identification techniques made possible to
monitor the current state or damage state of the structures. The GA is a parallel and global search technique that searches multiple points and makes no assumption about the search space. However, GAs are inherently slow and are poor at hill-climbing. In order to compensate the computational inefficiency in hill-climbing when the solution yielded by GA approaches the optimal value, a new identification strategy, combining GA and extended Kalman filter is proposed in this study. The analysis of extended Kalman filter is performed after the initial values of state variables used in the analysis are acquired after the evolution process of GA for each generation. Accordingly, a new method, called GA based extended Kalman filter is proposed. The proposed algorithm is explored by comparing the results of the predicted response with the measured response for both the SDOF linear/nonlinear system and the MDOF linear/nonlinear system with or without noise contamination. Finally, the hybrid computational strategy is also applied to the Taiwan Electricity Main Building using records of the 331 earthquake (2002) and the Civil-Environment Building of Chung-Shin University using records of one aftershock of Chi-Chi earthquake. The comparisons are made between the predicted acceleration and the measured one in the time domain and frequency domain for each case. Accordingly, the feasibility of the proposed new method is verified. |
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| 口試日期 | 2007-07-30 | 繳交日期 | 2007-08-28 | |