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研究生:張哲維
研究生(外文):Tse-Wei Chang
論文名稱:類神經網路於有限元素模式修正之應用
論文名稱(外文):Application of the Artificial-Neural-Network to Finite Element Model Updating
指導教授:陳振華陳振華引用關係
指導教授(外文):Chern-Hwa Chen
學位類別:碩士
校院名稱:中原大學
系所名稱:土木工程研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2001
畢業學年度:89
語文別:中文
論文頁數:167
中文關鍵詞:模態分析現地試驗類神經網路
外文關鍵詞:artificial neural networkfield testfinite element model updating modal analysis
相關次數:
  • 被引用被引用:4
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中文摘要
結構物施工完成後,常因許多原因而導致結構物發生勁度偏離當初設計的安全範圍或是變異,以致兩者間有所差異,在這種情形下,如果遇到地震或其他外力因素,就可能會對結構物造成危險的威脅。因此如果有適當結構系統的修正方法,能夠尋找出設計結構物與真實結構物兩者間是否已經產生勁度上的改變,其改變的程度是多少,則我們就可以據此來修正原先設計時的結構分析模型,重新評估結構物的安全性。
有限元素法分析是現代之土木結構設計分析最常用之方法。依據有限元素法建立真實結構物的設計分析模型,但此分析方法與實際結構之差異無法掌握,要確定分析結果之可用性,除了依照經驗及常理來判斷外,最可靠的方法,就是對受分析橋體進行現地實驗,再將現地試驗分析結果與有限元素分析所得者作比較。
本文是以楊氏模數、面積慣性矩之變化模擬結構物勁度之改變,並以PC版SAP90套裝軟體,建立結構有限元素法模型,並進行該結構模型之模態分析,再以類神經網路訓練,建立結構物桿件元素變異情形與動態特性之關連性,爾後一旦獲取經由受測結構物經現地試驗所得到的動態特性時,即可藉由類神經網路對結構物之元素作變異位置、勁度偏離量之判別。本文研究發現,以有限元素法配合類神經網路做為結構分析模型之修正工具,將是一個可行的方法。
A finite element model updating methodology based on an artificial neural network is proposed in this paper. The artificial neural network constists of three layers, namely input layer, hidden layer, and output layer, The structural dynamic characteristics (e.g. frequencies, modal shapes) are used to train the artificial neural network by using Back-Propagation Network. technique Then, the weighting matrices in the network are used to determine the system characteristics. The proposed procedure is demonstrated in processing numerical simulation data and measured data of a steel arch tower of cable-stayed bridge.
目 錄
中文摘要 Ⅰ
英文摘要 Ⅱ
誌 謝 Ⅲ
目 錄 Ⅳ
表 目 錄 Ⅷ
圖 目 錄 Ⅹ
第一章 緒論 1
1.1 前 言 1
1.2 研究動機與研究目的 2
1.3 本文架構 3
第二章 文獻回顧 4
2.1 前 言 4
2.2 有限元素模式修正方法 4
2.3 類神經網路 8
2.4 現地實驗方法 12
第三章類神經網路 14
3.1 前 言 14
3.2 類神經網路之簡介 14
3.2.1 生物神經網路 14
3.2.2 類神經網路基本理論 15
3.2.3 類神經網路學習規則與模式 18
3.3 倒傳遞神經網路之原理架構 20
3.4 勁度差值 分析 25
3.4.1 逆振態擾動法 25
3.4.2 簡支梁實例分析 27
第四章修正程序與數值模擬 29
4.1 前 言 29
4.2 有限元素分析模式修正討論 29
4.3 有限元素模式的建立 31
4.4 模擬分析結果 31
4.5 頻率與振態的求解過程 32
4.6 類神經網路訓練與收斂 36
4.6.1 資料之選取與處理 36
4.6.2 網路之訓練與建構 38
4.6.3 類神經網路之設定 38
4.6.4 範 例 (一) 39
4.6.5 範 例 (二) 40
4.6.6 範 例 (三) 41
4.6.7 執行學習過程 42
4.7 學習結果與預測 43
4.7.1 範 例 (一)結果 43
4.7.2 範 例 (二)結果 44
4.7.3 範 例 (三)結果 45
第五章貓羅溪斜張橋修正過程 47
5.1 前 言 47
5.2 貓羅溪斜張橋介紹 47
5.3 有限元素分析模型的建立 48
5.3.1 頻率差值與振態差值 49
5.4 現地實驗與動力特性系統識別 50
5.4.1 微動試驗 50
5.4.2 動力特性之系統識別 52
5.4.2.1 時間序列模式與建模 52
5.4.2.2 系統動態特性之估算 54
5.4.3 現地試驗分析之結果與比較 56
5.5 第一階段修正過程 57
5.6 第二階段修正過程 58
5.6.1 資料之選取與處理 58
5.6.2 網路之訓練與建構 59
5.6.3 個別元素的修正過程 59
5.6.4 全部元素之對稱修正過程 61
5.6.5 全部元素之不對稱修正過程 62
5.7 學習結果與預測 62
5.7.1 個別元素之修正結果 62
5.7.2 全部元素之對稱修正結果 63
5.7.3 全部元素之不對稱修正結果 64
5.8 結 論 62
第六章結論與建議 66
6.1 結 論 66
6.2 建 議 67
參考文獻 69
附 表 75
附 圖 100
作者簡歷 167
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