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系統識別號 U0026-2308201013153800
論文名稱(中文) 基因演算法在受損結構行為分析上之應用
論文名稱(英文) Applying Genetic Algorithms on Behavior analysis of Damaged Structures
校院名稱 成功大學
系所名稱(中) 土木工程學系碩博士班
系所名稱(英) Department of Civil Engineering
學年度 98
學期 2
出版年 99
研究生(中文) 劉中杰
研究生(英文) Zhong-Jei Liu
學號 n6890105
學位類別 博士
語文別 中文
論文頁數 122頁
口試委員 指導教授-徐德修
口試委員-張國鎮
口試委員-洪士林
口試委員-葉怡成
口試委員-朱聖浩
口試委員-朱世禹
中文關鍵字 非破壞檢測  基因演算法  不完全量測 
英文關鍵字 non-destructive detection  genetic algorithm  incomplete measurement 
學科別分類
中文摘要 本研究旨在探討使用加速度計量測結構反應並以基因演算法求取結構勁度之非破壞檢測方式。文中基因演算法的適應函數是以加速度計量測反應與結構反應之相似度為基準以求得最佳基因並獲得破壞結構之勁度。研究中並探討不完全量測及具雜訊之處理方式並針對一四層樓結構為例子,包括加速度計數量不完全及量測值包含雜訊之情形,進行實例演算。最後研究亦發現即使加速度計部分損壞導致數量不完全或是量測資料包含雜訊時,基因演算法仍能藉由其它資料求得最佳解。文中亦針對一個鋼筋混凝土實驗模型藉由基因演算法的非破壞檢測做實例分析,落實基因演算法在實際應用上之可行性。

  由於液流阻尼器的低成本以及不會產生應力增加結構負擔的特性,近年來已逐漸受到重視。雖然結構的行為以線性模擬之,但加裝液流阻尼器結構之數值分析最主要的問題在於其隨著速度而改變的出力為高度非線性行為,也因此導致整個結構的行為呈現非線性。隨機線性法(Stochastic Linearization Technique) 是目前最常被採用的方法,其以一個等值線性阻尼之線性反應取代了原來的非線性結構。在此我們嚐試以近年來常被採用做最佳化搜尋的基因演算法求得滿足運動平衡方程式之最佳解,並求得加裝液流阻尼器結構之非線性反應。最後並針對加裝液流阻尼器結構做非破壞檢測及探討。
英文摘要 The research describes the non-destructive damaged structures using the acceleration sensor measurement data for four-story shear structures, and obtains the optimal solution of story stiffness by genetic algorithm. The fitness function, based on the similarity of measurement-curve derives from the acceleration sensor with responses of the considered structure to find the optimal gene and the stiffness of the damaged structure. The research aims a four-story shear typed structure, including cases with incomplete acceleration sensors. Cases with measurement data noises are also discussed. The simulation results identify the stiffness of damaged-stories using fewer displacement sensors if the sensor location is properly arranged. The genetic algorithm proves that neglecting the noise of incomplete measuring data from other sensing points can still successfully obtains the optimal solution. The current study discusses the experiment case of the four-story reinforced concrete structure, and finds the stiffness reduction by the genetic algorithm.

Fluid dampers have received considerable attention in recent years because of their low cost and usually will not induce additional here in structural systems. However, analysis of structures with fluid damper devices reveals that we are facing a nonlinear dynamic problem because of the relationship between force and velocity of the damper is highly nonlinear, although the structure behaves linearly, the whole damper-structure system has inherent nonlinear properties. The stochastic linearization technique (SLT) is typically performed with a linear system that is statistically equivalent to the nonlinear one. In this paper, an alternative technique, a genetic algorithm (GA), is applied, which is the most used optimal searching method in recent years. The motion equation of the structures is utilized as the objective function. And the non-destructive detection of structure with fluid damper devices are also detected herein.
論文目次 摘要...............1
Abstract..........2
誌謝................4
目錄................6
表目錄............8
圖目錄............9
緒論...............14
第一章 前言..........17
1.1非破壞檢測之文獻回顧..............17
1.2 液流阻尼結構之文獻回顧..........21
1.3 基因演算法..............22
第二章 基因演算法之應用以及數值積分法(線性加速度法)簡介......24
2.1染色體和基因的定義..........24
2.2 基因演算法及其參數的探討............26
2.2.1 適應函數的建立........26
2.2.2 適應函數的調整..............27
2.2.3 基因個體數目的調整..........33
2.2.4 輪盤法則及交配率..........36
2.2.5 陷入局部最佳解的突變機制及參數....37
2.2.6 菁英法則..............39
2.3 結構反應之數值模擬..........39
2.3.1 線性加速度法..............39
2.3.2 多自由度結構動力反應方程式............41
第三章 數值例分析................43
3.1 一樓勁度衰減之數值例分析..........44
3.2 一、二樓勁度衰減之數值例分析......45
3.3 一、二、三樓勁度衰減之數值例分析..........45
3.4 雜訊干擾的影響..............46
3.5 三層樓剪力結構之樓層勁度衰減數值例分析....49
第四章 液流阻尼結構的非線性行為及非破壞檢測......55
4.1 以基因演算法求解液流阻尼結構反應..........55
4.2 液流阻尼結構反應數值解介紹......57
4.3 S.S.P、S.L.T、G.A三種數值方法之比較..........65
4.3.1 S.L.T法之迭代........65
4.3.2 單自由度結構........66
4.4 多自由度結構............71
4.5 液流阻尼結構非破壞檢測數值例............77
第五章 基因演算法在高樓層結構之應用及探討........79
5.1 十層樓數值例分析..............79
5.2八層樓數值例分析..........86
5.3 自由度和基因演算法演算所需世代的關係........94
第六章 結論及展望................96
參考文獻................99
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