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論文中文名稱:基因演算法和類神經網路在斜張橋最佳化設計及健康診斷之應用 [以論文名稱查詢館藏系統]
論文英文名稱:Application of Genetic Algorithm and Neural Network to Optimal Design together with Health Diagnosis of Cable-Stayed Bridge [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:工程學院
系所名稱:土木與防災研究所
中文姓名:林祐川
英文姓名:Yu-Chuan Lin
研究生學號:92428025
學位類別:碩士
語文別:中文
口試日期:2005-07-04
論文頁數:174
指導教授中文名:宋裕祺
指導教授英文名:Yu-Chi Sung
口試委員中文名:蔡益超;呂良正;林主潔
中文關鍵詞:斜張橋最佳化設計結構診斷基因演算法類神經網路
英文關鍵詞:Cable-Stayed BridgeOptimal DesignHealth DiagnosisGenetic AlgorithmArtificial Neural Network
論文中文摘要:人工智慧(Artificial Intelligent )發展迄今已半世紀,由於電腦運算速度的日益提升使其在各領域的應用在最近幾年更加廣泛,本文採用人工智慧中的基因演算法( Genetic Algorithm)和類神經網路( Artificial Neural Network )作為研究工具。其中基因演算法在處理最佳化問題上有很好的能力,該方法會依照問題特性利用啟發性的搜尋方式來尋求最佳解,所需計算時間也較其他最佳化理論所需者快速,加上基因演算法本身具有跳脫局部極值的能力,這些優點都是基因演算法逐漸受到重視的原因。而類神經網路憑藉其優良的自學習和自適應能力,在範例資料健全的情況下能從中整理出正確的對應規則,使其處理複雜的對應問題上有極高的評價。
本文利用基因演算法的優點和特性來協助我們求解結構最佳化設計的問題。以往斜張橋最佳化設計多是利用數學規劃法來求鋼索預拉力的組合,本文則分別採用基因演算法和混合基因演算法( Hybrid Genetic Algorithm)進行求解。至於結構健康診斷則分別利用類神經網路和基因演算法進行靜態識別,其方法是藉由斜張橋橋拱頂部的轉角來反推纜索的內力組合,並求出各構件對應的安全係數,作為結構健康診斷的依據,所得之結果冀能提供為類似工程參考之用。
論文英文摘要:Genetic algorithm (GA) and Neural Networks (NN) are two important approaches of artificial intelligence (AI) to deal with highly nonlinear problems effectively. With the strong ability in searching global minimum or maximum, GA is recognized as a powerful procedure for optimization. With the significant ability in learning, NN is able to reflect the nonlinear mapping relationships between input and output. As a result, GA and NN have been successfully applied to various engineering field. However, few of their applications to cable-stayed bridge were found. This thesis thus focuses on the applications of GA and NN on the structural optimal design and structural health diagnosis of cable-stayed bridge.
Based on the structural optimization approach, this thesis uses the theory of minimum strain energy of the bridge in deriving the objective function as the quadratic form of the post-tensioning cable forces. In addition, the equality constraints for the restriction on the displacements of the pylon and the un-equality constraints for the limitation on the envelopes of the cable forces are both implemented in the optimization model. GA is then conducted to find the post-tensioning cable forces of the bridge for the structural optimal design. Besides, GA and NN are used, respectively, to find the corresponding post-tensioning cable forces of the bridge subject to the measured rotations of pylon for the structural health diagnosis.
The Mau-Lo Hsi Cable-stayed Bridge is adopted as a case study. The results obtained revealed that the presented method indeed fulfill the structural optimal design and the structural health diagnosis and might be a useful reference for similar bridge engineering.
論文目次:中文摘要 i
英文摘要 ii
誌謝 iv
目錄 v
圖目錄 vii
表目錄 xiii
第一章 緒論 1
1.1 研究目的與動機 1
1.2 研究方法 2
第二章 文獻回顧 3
2.1 基因演算法在結構最佳化設計之應用 3
2.2 類神經網路與基因演算法在結構健康診斷之應用 4
2.2.1 類神經網路在結構健康診斷之應用 4
2.2.2 基因演算法在結構健康診斷之應用 6
第三章 基因演算法之理論及應用 7
3.1 基因演算法的基本概念 7
3.2 基因演算法運作流程 9
3.2.1 基因編碼 10
3.2.2 適應度函數(fitness function) 12
3.2.3 選擇與複製(selection & reproduction) 18
3.2.4 交換(crossover)/重組(recombination) 20
3.2.5 突變(mutation) 24
3.2.6 菁英保存策略(elistist model) 26
3.3 混合基因演算法(hybrid genetic algorithm) 26
3.4 基因演算法的特點 28
3.5 基因演算法用於數值最佳化之案例分析與驗証 29
3.6 基因演算法用於結構最佳化之案例分析與驗証 39
第四章 斜張橋最佳化模式的建立和求解 42
4.1 斜張橋之力學特性 43
4.2 斜張橋最佳化設計的目標函數和約束條件 44
4.3 斜張橋最佳化模式之建立 45
4.4 案例分析 48
4.4.1 案例對象介紹 48
4.4.2 最佳化分析結果 51
4.4.3 小結 57
第五章 斜張橋之健康診斷 58
5.1 貓羅溪斜張橋的診斷 58
5.2 以類神經網路處理結構診斷 60
5.2.1 類神經網路簡介 60
5.2.2 類神經網路專家群組處理貓羅溪斜張橋之健康診斷 62
5.3 以基因演算法處理結構健康診斷 68
5.3.1 診斷模型的建立 68
5.3.2 各構件之安全係數之計算 69
5.3.3 基因演算法應用於貓羅溪斜張橋健康診斷之分析 70
5.4 小結 75
第六章 結論與建議 77
6.1 結論 77
6.2 建議 77
參考文獻 79
附錄 84
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