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碩士論文公告區 |
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| 年度： | 93 | |
| 姓名： | 洪忠儀(Jhong-Yi Hong) | |
| 論文題目(中)： | 結合基因演算法與局部搜尋法於結構動力系統識別 | |
| 論文題目(英)： | Application of Genetic Algorithm and Local Search Method to Structural Dynamic System Identification | |
| 摘要(中)： | ||
| 現今預測地震的科技尚未臻成熟，基於安全之考慮，目前只能依照法規進行耐震設計以防患未然。結構物的動態行為是耐震設計與分析中所考慮的重要因素之一，因此，近年來發展了各種不同的系統識別方法與模式，以求得結構系統受震後之動力行為與系統參數的變化。 本研究利用結合基因演算法與局部搜尋法的混合運算策略進行動力系統參數識別。首先建立單自由度線性、非線性與多自由度線性、非線性模型，再利用數值模擬的方式產生地震記錄與不同系統的量測反應，接著提出結合基因演算法的全域搜索及局部搜尋法的區域搜尋之混合運算策略，藉由此混合運算策略不僅可以搜尋出符合該系統的系統參數而且可以加快收斂率。為了要更接近真實地震的狀況，並於輸入及輸出反應中加入適當的雜訊來探討其識別結果，以驗証出該混合運算策略應用於實際建築物動力特性系統識別之可行性。 接著利用該混合運算策略並使用振態參數識別法對實際結構物-台電大樓進行振態參數識別，地震紀錄則是使用台電大樓於311地震時所蒐集到的地震反應。利用單向擾動系統、多向擾動系統以及以多個樓層反應當作輸出的系統，採用結合基因演算法與局部搜尋法的混合運算策略進行振態參數識別，並將其振態參數與該大樓的頻率內涵相比較，均有不錯的識別結果。 |
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| 摘要(英)： | ||
| 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. Because of their devastating potential, there is a great interest in predicting the location and time of large earthquakes. Although a great deal is known about where earthquakes are likely to occur, there is currently no reliable way to predict the time when an event will occur in any specific location. However, the damages caused by them can be greatly reduced with proper structural design using safer seismic code. In this regard, dynamic behavior of structures under earthquakes should be considered in the process of design. In order to realize the dynamic behavior of structural systems, we can determine the dynamic models and parameters by system identification techniques. In the past few decades, many optimization techniques have been employed for system identification problems. Most of the identification methods mentioned above are calculus-based search method. They are performed by point-to-point search strategy. A good initial guess of the parameter and gradient or higher-order derivatives of the objective function are normally required. There is a possibility to fall into a local minimum rather than the global minimum. On the contrary, genetic algorithms (GAs) are optimization procedures inspired by natural evolution. They model natural processes, such as selection, recombination, and mutation, and work on populations of individuals instead of single solutions. In this way, the algorithms are parallel and global search techniques that search multiple points, so they are more likely to obtain a global solution. While the GA method has been developed as a powerful search tool in a global solution space, it is not necessarily efficient in fine-tuning for local convergence particularly when the search domain is large. In order to accelerate the convergence to the optima solutions, a hybrid identification strategy, combining GA and local search technique such as Gauss-Newton method is proposed in this study. 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 from the 331 earthquake (2002). The comparison is made between the predicted acceleration and the measured one for each case. |
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| 檔案： | 沒有相關檔案 | |
| 相關連結： | http://ethesys.lib.cyut.edu.tw/ETD-db/ETD-search-c/view_etd?URN=etd-0829105-095359 | |