本研究利用結合基因演算法與局部搜尋法的改良型基因演算法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、非線性之系統模型,再利用數值模擬的方式產生地震記錄與對應各系統的量測反應,接著提出結合基因演算法的全域搜索及高斯-牛頓法的局部搜尋之混合運算策略,稱為改良型基因演算法,藉由此新式演算法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速率。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,以驗証出改良型基因演算法應用於實際建築物動力特性系統識別之可行性。接著利用該改良型基因演算法並配合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 地震時所蒐集到的量測紀錄。利用單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改良型基因演算法進行振態參數識別,並將其振態頻率與該大樓的振幅譜相比較,顯示均有不錯的識別結果。 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 optimal 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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中華民國第九屆結構工程研討會論文集=Proceedings of The 9th National Conference on Structure Engineering,10頁