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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 羅俊雄(Chin-hsiung Loh) | |
dc.contributor.author | I-No Yu | en |
dc.contributor.author | 余以諾 | zh_TW |
dc.date.accessioned | 2021-06-17T03:15:18Z | - |
dc.date.available | 2021-07-19 | |
dc.date.copyright | 2018-07-19 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-07-08 | |
dc.identifier.citation | [1] E. Safak, “Adaptive modeling, identification, and control of dynamic structural system, I: theory”, J. Eng. Mech. ASCE 115, 2386-2405 (1989).
[2] P. Andersson, “Adaptive forgetting in recursive identification through multiple models”, Int. J. Control, Vol.42, No.5, 1175-1193 (1985) [3] Qijun Xia, Ming Rao, Yiqun Ying and Xuemin Shen, “Adaptive Fading Kalman filter with an application” Automatica, Vol.30, No.8, 1333-1338. (1994) [4] C. H. Loh and H. M. Lin,”Application of off line and on line identification techniques to building seismic response data” Earthquake Engineering and Structural Dynamics, Vol.25, No.3,.269-290. (1996). [5] C.H. Loh, C.C. Huang and C.Y. Lin, “Time Domain Identification of Frames under Earthquake Loading” ASCE, J. of Engineering Mechanics, Vol.126, No.7, 693-703. (2000). [6] J.D. Chen, “Application of Online Recursive Subspace Identification on Structural Stiffness Assessment and Quantification”, Master thesis, National Taiwan University (2010). [7] CJ M. Delgado, PLD Santos. Recursive canonical variate subspace algorithm, in SICE 2004 Annual Conference. 2004: 2566–2571. [8] M Verhaegen, P Dewilde, Subspace model identification Part I. The output-error state-space model identification class of algorithms. Int. J. Control. 1992; 56(5): 1187–1210. [9] J.N. Juang, “Applied System Identification”, Prentice Hall PTR, Upper Saddle River, NJ, USA, (1994). [10] P Van Overschee, B De Moor, N4SID: Subspace Algorithms for the Identification of Combined Deterministic-Stochastic Systems. Automatica. 1994; 30(1): 75–93 [11] P Van Overschee, B De Moor, W Favoreel, Numerical algorithms for subspace state space system identification (N4SID), in Proceedings of the ASME Design Engineering Technical Conference (ASME DETC’97), 1997, no. Guidorzi 1975. 9. [12] D. C. Zimmerman and M. Kaouk, “Structural damage detection using a minimum rank update theory,” Journal of Vibration and Acoustics, Vol. 116, No. 2, 222-231 (1994). [13] Hiroshi Oku, Hidenori Kimura, “A recursive 4SID from the input-output point of view”, Asian Journal of Control, Vol. 1, pp258-269. (1999). [14] Hiroshi Oku, Hidenori Kimura, “Recursive 4SID algorithms using gradient type subspace tracking”, Automatica, 38: pp1035–1043, (2002). [15] Jun-Da Chen & Chin-Hsiung Loh, “Tracking Modal Parameters of Building Structures from Experimental Studies and Earthquake Response Measurements,” Int. J. of Structural Health Monitoring, DOI: 10.1177/ Vol. 16(5) 551–567 (2017) [16] Chin-Hsiung Loh & Jun-Da Chen, “Tracking Modal Parameters from Building Seismic Response Data Using Recursive Subspace Identification Algorithm,” Published in Int. J. Earthquake Engineering & Structural Dynamics, DOI: 10.1002/eqe.2900, 46:2163–2183 (2017) [17] Jun-Da Chen & Chin-Hsiung Loh, “Two‐stage damage detection algorithms of structure using modal parameters identified from recursive subspace identification,” Int. J. Earthquake Engineering & Structural Dynamics, DOI: 10.1002/eqe.2980 (2017) (accepted) [18] Bauer, R. J. “Extracting mass, stiffness and damping from identified state-space matrices”, Proceedings of CANCAM99, Hamilton, Canada. (1999). [19] D. C. Zimmerman and M. Kaouk, “Structural damage detection using a minimum rank update theory,” Journal of Vibration and Acoustics, Vol. 116, No. 2, 222-231 (1994). [20] Kentaro Kameyama et al. “Recursive 4SID-based identification algorithm with fixed input-output data size.” International Journal of Innovative Computing,Information and Control, Vol. 1, No. 1, pp17–33, (2005). [21] M. Tamaoki, K. Akizuki, K. Oura, “Order and parameter estimation of time-varying system by subspace method”, Electrical engineering in Japan, Vol. 157, No. 2, pp57-64, (2006). [22] J.C. Willems, “From time series to linear systems”, Automatica, Part I: 22(5), pp561-580, (1986); Part II: 22(6), pp675-694, (1986); Part III: 23(1), pp87-115,(1987). [23] Peter Van Overschee, Bart De Moor, “Subspace identification for linear systems:theory-implementation-applications”,Boston/London/Dordrecht: Kluwer Academic Publishers, (1996). [24] Michel Verhaegen, “Identification of the deterministic part of MIMO state spacemodels given in innovations from input-output data”, Automatica, 30(1): pp61–74,(1994). [25] J.H. Weng, “Application of Subspace Identification in System Identification and Structural Damage Detection”, Doctoral Dissertation, National Taiwan University,(2010). [26] Hiroshi Oku, Hidenori Kimura, “A recursive 4SID from the input-output point of view”, Asian Journal of Control, Vol. 1, pp258-269. (1999) [27] Hiroshi Oku, G. Nijsse, M. Verhaegen, and V. Verdult. “Change detection in the dynamics with recursive subspace identification”, Proceedings of the 40th IEEE CDC, Orlando, Florida, pp2297-2302. (2001). [28] Kentaro Kameyama et al. “Recursive 4SID-based identification algorithm with fixed input-output data size.” International Journal of Innovative Computing, Information and Control, Vol. 1, No. 1, pp17–33, (2005). [29] E. Reynnders, G. De Roeck. “Reference-based combined deterministic-stochastic subspace identification for experimental and operational modal analysis”, Mechanical systems and signal processing, Vol 22, pp 617-637, (2008). [30] M. Tamaoki, K. Akizuki, K. Oura, “Order and parameter estimation of time-varying system by subspace method”, Electrical engineering in Japan, Vol. 157, No. 2, pp57-64, (2006). [31] J.H. Weng, “Application of Subspace Identification in System Identification and Structural Damage Detection”, Doctoral Dissertation, National Taiwan University, (2010). [32] Huang, N. E., Z. Wu, S. R. Long, K. C. Arnold, K. Blank and T. W. Liu, 2005: On Instantaneous Frequency, (Submitted to Proc. Roy. Soc. Lond.) [33] J.M. Caicedo, S.J. Dyke and E. A. Johnson., “Natural excitation technique and eigensystem realization algorithm for phase I of the IASC-ASCE benchmark problem: simulated data”, Journal of Engineering Mechanics, Vol.130, No.1, pp49-60, (2004). [34] K.V. Yuen, “Efficient model correction method with modal measurement,” Journal of Engineering Mechanics, Vol. 136, No. 1, pp91-99, (2010). [35] Y.C. Wu, “Damage detection using mode shape expansion and finite element model updating”, Master thesis, National Taiwan University (2010). [36] Lee, Z. K., Wu, T. H. and Loh, C. H. (2003). “System identification on the seismic behavior of an isolated bridge”, Earthquake Engineering and Structural Dynamics, Vol. 32, No. 12, pp. 1797–1812. [37] C.H. Loh, N.R. Yeh, J.D. Chen, T.H. Wu, “Tracking modal parameters from building seismic response measurements”, EWSHM 2016. (2016) [38] K.C. Tsai, S.J. Hwang,” SEIMIC RETROFIT PROGRAM FOR TAIWAN SCHOOL BUILDINGS AFTER 1999 CHI-CHI EARTHQUAKE” The 14th World Conference on Earthquake Engineering October 12-17, 2008, Beijing, China [39] W.T. Hsu, “Automatic (operational) modal analysis for Stochastic Subspace Identification”, Master thesis, National Taiwan University. (2015) [40] C.H. Mao, “Nonlinear system identification method for structural health monitoring : techniques for the detection of nonlinear indicators.”, Master thesis, National Taiwan University. (2009) | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/69422 | - |
dc.description.abstract | 本研究主要目的為,基於遞迴子空間識別法(Recursive Subspace Identification, RSI)與二階段結構損傷識別理論,提出一套自動化結構健康監測的方法,本方法可以分為四大部分,第一,利用頻率響應函數(Frequency Response Function, FRF)進行希爾伯特轉換(Hilbert Transform, HT),透過其差異性來判斷系統是否進入到非線性行為。第二,若結構物進入到非線性行為,則開始進行損傷評估,利用自動化主波監測(Automatic Dominant-Wave Arrival Detection)來決定遞迴子空間識別法的起始資料點。第三,將遞迴子空間識別法所識別的模態參數(Modal Parameter)進行二階段結構損傷理論,計算結構物勁度折減。第四,評估結構損傷狀態,根據美國聯邦緊急事務管理署提出的規範Hazus-HM2.1,由層間變位判別損傷狀態,此階段目前應用於實驗結構物。在進行自動化分析時,遞迴子空間系統識別法在部分窗函數訊號不穩,導致識別結果的不連續性,會造成二階段結構損傷識別理論的分析困難。本文提出三個唯輸出(Output-Only)訊號分析方法,可以應用於重建模態參數的連續性。本研究透過提出的自動化結構健康監測方法,應用於實驗以及實際結構,成功的識別損傷位置與時間,並量化損傷狀態。 | zh_TW |
dc.description.abstract | The purpose of this research is to proposed an automatic health monitoring method, based on Recursive Subspace Identification (RSI) method and two-stage damage detection algorithm. This method can be divided into three parts: First, use of the difference between frequency response function and Hilbert transform of frequency response function to determine if the structure become non-linear behavior or not. Second, if structures have become non-linear behavior, use the Automatic Dominant-Wave Arrival Detection to decide the starting data point of recursive subspace identification analysis. Third, Use the modal parameter identified by RSI method to conduct two-stage damage detection algorithm and calculate the stiffness reduction. Fourth, according the Hazus-MH2.1 published by Federal Emergency Management Agency, the damage-state of generic buildings were defined in the value of average inter-story drift ratio. While using the automatic structural health monitoring method, the unstable of RSI identified results will cause the difficulty of two-stage damage detection algorithm. This research proposed three output-only signal analysis methods to apply on the reconstruct the continuity of the identified results of RSI. The research applies automatic structural health monitoring method on both experimental structure and practical structure to successfully localize and quantify the damage, and the damage occurrence during the earthquake excitation. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T03:15:18Z (GMT). No. of bitstreams: 1 ntu-107-R05521229-1.pdf: 11631295 bytes, checksum: 46bc910a49b47c94e6ba63f55b721156 (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 口試委員會審定書
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS v LIST OF FIGURES ix LIST OF TABLES xiv Chapter 1 Introduction 1 1.1 Background 1 1.2 Literature Review 2 1.3 Research Objectives 4 Chapter 2 Recursive Subspace Identification Algorithm 6 2.1 Introduction 6 2.2 Subspace Identification Algorithm 7 2.2.1 State Space Model of a Structural System. 7 2.2.2 Formation of Data Hankel Matrix and Matrix Input-Output Equations 10 2.2.3 Extraction of extended observability matrix: 14 2.2.4 Extraction of Modal Parameters 20 2.3 Recursive Subspace Identification Algorithms (RSI) 22 2.3.1 RSI using Matrix Inversion Lemma renewing method on Oblique Projection (RSI-Inversion-Oblique) 22 2.3.2 RSI using Bona-fide renewing method on Oblique Projection (RSI-BonaFide-Oblique) 26 2.3.3 Suggestions on Assigning User-defined Parameters 30 2.4 Physical meaning of the results of RSI 38 2.4.1 Introduction 38 2.4.2 Description of the bilinear SDOF system 39 2.4.3 Selected User-defined Parameter 39 2.4.4 Estimation of Reference Dynamic Characteristics 41 2.5 Compensation of dis-continuity on RSI Modal frequency identification 43 2.5.1 Introduction 43 Chapter 3 Two-Stage Damage Detection Algorithm 47 3.1 Introduction 47 3.2 Fundamental Damage Assessment Method 47 3.2.1 Least Squares stiffness method (LSSM) 47 3.2.2 Efficient Model Correction Method (EMCM) 50 3.3 Two-Stage Damage Detection Algorithm 55 3.3.1 Strategy-1 in Two-Stage Damage Detection Algorithm: Reference Model+EMCM 56 Chapter 4 Experimental Study 60 4.1 Introduction 60 4.2 Shaking Table Test of a 3-story Reinforced Concrete Structure 60 4.2.1 Description of the Experimental Structure 60 4.2.2 Selected User-defined Parameters 61 4.2.3 Two-Stage Damage Detection Algorithm 63 4.2.4 Criterion of Structure Damage Stage 65 4.3 Application of automatic monitoring method 68 4.4 Conclusion 69 Chapter 5 Practical Study 71 5.1 Introduction 71 5.2 Seismic Data of Bai-Ho isolated bridge in Tainan County 72 5.2.1 Description of the structure 72 5.2.2 Application of RSI-Inversion Oblique 73 5.2.3 Two-Stage Damage Detection Algorithm 76 5.2.4 Compensation of RSI identified frequency 80 5.2.5 Summary 82 5.3 Ming-Li elementary school 83 5.3.1 Description of the structure 83 5.3.2 Selected User-defined parameters 85 5.3.3 Two-Stage Damage Detection Algorithm 88 5.3.4 Summary 90 5.4 NCTU Government Employee Housing 91 5.4.1 Description of the Structure 91 5.4.2 Selected user-defines Parameters 91 5.4.3 Two-stage Damage Detection Algorithm 94 5.4.4 Summary 97 Chapter 6 Conclusions 99 6.1 Research Conclusions 99 6.2 Recommendation for Future Work 100 References 101 | |
dc.language.iso | en | |
dc.title | 應用子空間系統識別法於結構安全性評估 | zh_TW |
dc.title | Seismic Safety Assessment of Structures using Recursive Subspace Identification | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張家銘(Chia-Ming Chang),許丁友(Ting-Yu Hsu) | |
dc.subject.keyword | 自動化結構健康監測,遞迴子空間識別法,線上系統識別,結構損傷識別,損傷指標, | zh_TW |
dc.subject.keyword | Automatic structural health monitoring method,Recursive Subspace Identification,Online System Identification,Structure damage detection,Damage Index, | en |
dc.relation.page | 156 | |
dc.identifier.doi | 10.6342/NTU201801382 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2018-07-09 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
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