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研究生:陳聖夫
研究生(外文):Sheng-Fu Chen
論文名稱:盲訊號識別法於結構健康診測之實驗研究
論文名稱(外文):Experimental Study on Structural Health Monitoring using Blind Source Separation Method
指導教授:羅俊雄羅俊雄引用關係
口試委員:田堯彰張國鎮
口試日期:2015-06-29
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:土木工程學研究所
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:2015
畢業學年度:103
語文別:英文
論文頁數:144
中文關鍵詞:盲訊號分離二階統計盲訊號識別法系統識別結構健康監測損傷檢測ABAQUS CAE
外文關鍵詞:Blind Source SeparationSecond Order Blind IdentificationSystem IdentificationStructural Health MonitoringDamage detectionABAQUS CAE
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大部分情況下,結構物之受力情形難以量測,因此唯輸出 (Output-only) 系統識別方法近年來發展迅速,本研究目的為探討二階統計盲訊號識別法 (Second Order Blind Identification, SOBI) 於土木結構系統識別及損壞診斷的應用範疇。
本研究第一階段旨在檢測二階統計盲訊號識別法 (SOBI) 應用於非時變性與時變性系統上的表現並與隨機子空間識別法 (Covariance Driven Stochastic Subspace Identification, SSI-COV)比較其優缺點。本文將二階統計盲訊號識別法 (SOBI) 應用於數值模擬及實地沖刷試驗上:(1) 不同車輛載重下之車橋互制系統(非時變性) (2) 橋樑沖刷實驗之損壞診斷與預警之應用(時變性系統)。識別出動態參數後並利用不同損壞指標診斷出破壞位置。
本研究第二部分旨在檢測二階統計盲訊號識別法在輕微非線性系統上的表現。二階統計盲訊號識別法 (SOBI) 分別被應用在兩棟三層樓鋼構架上。試體一為對稱鋼構架,利用二階統計盲訊號識別法 (SOBI) 識別出動態特性並結合 ABAQUS CAE 執行動態分析預測應力,除此之外還可利用識別出的模態貢獻及應力判斷系統是否進入非線性並與遲滯圈比較。試體二則是替換第一層樓西北角之柱子為弱柱之反對稱試體,由於是反對稱結構,除了X方向模態還激發出Y方向及扭轉方向模態,另外還發現結構激發出雙重模態(Double modes)的情形,最後利用識別出的模態貢獻及應力判斷系統是否進入非線性。


In most civil structures, the input excitation is hard to be measured. Thus, the output-only system identification has gained lots of interest. In this research the application of output-only system identification technique known as Second Order Blind Identification (SOBI) algorithm in civil structures is carried out.
The objective of the first part of this thesis is to inspect whether SOBI algorithm is workable on time-invariant and time-variant system when subject to either white noise excitation or earthquake excitation and compared the results to Covariance Driven Stochastic Subspace Identification (SSI-COV). SOBI algorithm is applied on: (1) Vehicle-bridge interaction system under different traffic loading (time-invariant system). (2) Steel bridge under continuous scour (time-variant system). Through the results of identification the dynamic characteristics of the system are verified and damage detection using different damage index is also investigated.
The objective of second part of this thesis is to inspect the identification ability of using SOBI algorithm when system turned into lightly non-linearity. A shaking table test of two 3-story steel structures are carried out (one is the symmetric structure and the other is the unsymmetrical structure). For specimen 1, dynamic characteristics identified by SOBI algorithm are combined with ABAQUS CAE to estimate the local property, such as member stress. For specimen 2, additional lateral and torsion modes are identified using SOBI and the result is compared with the subspace identification technique.


口試委員審定書
ACKNOWLEDGEMENTS I
ABSTRACT(IN CHINESE) II
ABSTRACT III
CONTENTS V
LIST OF TABLES VIII
LIST OF FIGURES IX

1 Introduction 1
1.1 Motivation 1
1.2 Literature Review 2
1.2.1 System Identification Method 2
1.2.2 Damage Index 3
1.3 Objective and Scope 3
2 Blind Source Separation: Second Order Blind Identification 5
2.1 Blind Source Separation Model 5
2.2 Relation between Modal Coordinate and Blind Source Separation 6
2.3 Basic Theory of Second Order Blind Identification (SOBI) 7
2.4 Joint Approximation Diagonalization (JAD) 10
3 Simulation and Experimental Studies using Second Order Blind Identification Algorithm 14
3.1 Simulation Study(time-invariant system): Vehicle-Bridge Monitoring under Traffic Loading 14
3.1.1 Description of Bridge-Vehicle Interaction System 15
3.1.2 Application of SOBI to Simulation Data of Bridge-Vehicle Interaction System 16
3.1.2.1 Normal Case 16
3.1.2.2 Abnormal Case 17
3.1.2.3 Inspection of covariance matrix with different delay time 19
3.1.3 Damage Detection Using Change of Mode Shape Curvature 20
3.1.4 Summary and Discussion 21
3.2 Experiment study(time-variant system): Bridge Pier Scouring Experiment 22
3.2.1 Bridge Pier Scouring Experiment 23
3.2.2 Application of SOBI to Experimental Data 24
3.2.3 Inspection of covariance matrix at different delay time 25
3.2.4 Damage Detection: Damage Location Identification 26
3.2.5 Summary and discussion 28
4 Application of SOBI to Earthquake Excitation of Steel Structure(nonlinear system) 30
4.1 Experiment Study: 3-story steel frame 30
4.2 System Identification using SOBI algorithm 31
4.2.1 Nonlinearity Detection for Specimen 1 33
4.2.1.1 Abaqus CAE model updating 34
4.2.1.2 Estimation of Local Property 35
4.2.2 Nonlinearity Detection for Specimen 2 36
4.2.2.1 Double modes 37
4.2.3 Inspection of covariance matrix at different delay time 38
4.3 Summary and discussion 38
5 Conclusions 40
5.1 Conclusions 40
5.2Recommendations for future work 42
References 44
Appendix A Dynamic characteristics identified from each leve of seismic data (specimen 1) 104
Appendix B Dynamic characteristics identified from each leve of seismic data (specimen 2) 114
Appendix C Covariance matrix of sources calculated from each level of seismic data with different delay time (specimen 1) 134
Appendix D Covariance matrix of sources calculated from each level of seismic data with different delay time (specimen 2) 139



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