簡易檢索 / 詳目顯示

研究生: 康展榮
Kang, Chan-Jung
論文名稱: 實尺寸調諧質量阻尼器整合風洞資料即時複合試驗及基於數位孿生之結構健康監測開發
Development of Real-Time Hybrid Testing of Full-Scale Tuned Mass Damper Integrating Wind Tunnel Data and Structural Health Monitoring Based on Digital Twins
指導教授: 朱世禹
Chu, Shih-Yu
學位類別: 博士
Doctor
系所名稱: 工學院 - 土木工程學系
Department of Civil Engineering
論文出版年: 2024
畢業學年度: 112
語文別: 英文
論文頁數: 302
中文關鍵詞: 結構振動控制硬體迴路模擬系統識別稀疏非線性動力識別法即時複合試驗數位孿生結構健康監測
外文關鍵詞: Structural vibration control, Hardware-in-the-loop simulation (HILS), System identification, Sparse identification of nonlinear dynamics (SINDy), Real-time hybrid testing (RTHT), Digital twin, Structural health monitoring (SHM)
相關次數: 點閱:48下載:3
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 隨著全球人口增加,都會區為緩解人口壓力高層建築的興建變得更加普遍,然而高層建築的結構系統較為細長,其整體勁度通常較低,因此減少風引起的振動成為一個重要課題。調諧質量阻尼器是高層建築中最常用的減振策略之一。然而,在實際應用中仍存在許多挑戰,例如調諧質量阻尼器的實際性能是否符合其初始設計,這需要通過實際實驗來進行驗證。然而,在實驗室進行實尺寸調諧質量阻尼器的減震性能測試是不切實際的,因為無法在風洞或振動台上建造真實建築同時安裝上全尺寸的阻尼器。因此大多數調諧質量阻尼器在正式安裝前並未經過實際性能測試。本研究以一個全尺寸100 噸的調諧質量阻尼器為例,提出了一個在正式安裝前進行調諧質量阻尼器的標準測試程序。該測試程序的第一步驟是進行調諧質量阻尼器的動態參數的識別。調諧質量阻尼器的動態參數通過現場自由振動試驗和振動台識別試驗結合系統識別方法兩種方式來確定,本文同時使用稀疏非線性動力識別法來識別調諧質量阻尼器的摩擦模型。此測試程序的另一個步驟是使用振動台的即時複合試驗來驗證調諧質量阻尼器在減少目標建築因風引起的振動方面的控制性能。即時複合試驗的概念是將高層建築替換為數值模型,同時將全尺寸調諧質量阻尼器保留在振動台上。通過對振動台的先進控制技術,可以模擬並驗證全尺寸調諧質量阻尼器與高層建築在受風力作用時的交互影響。考慮到即時複合試驗的高風險,本研究還提出使用硬體迴路模擬作為即時複合試驗的安全測試。硬體迴路模擬可幫助測試操作員在虛擬的即時複合試驗環境中預先測試任何硬體和軟體的穩定性。除了對阻尼器的性能測試外,本文還提出了一種基於數位孿生技術的結構健康監測架構,同時應用於調諧質量阻尼器和目標建築的長期維護。該過程包括使用智慧感測器進行遠程自動振動測量,通過線上識別分析來識別目標建築和調諧質量阻尼器的當前動態特性,並使用卡爾曼濾波估算作用在目標建築上的環境力,並提供即時 3D 動態可視化模型。

    As the global population increases, high-rise buildings are built more to alleviate population pressure. However, due to the slender structural systems of high-rise buildings, they generally have lower overall stiffness, making the reduction of wind-induced vibrations a crucial issue. Tuned Mass Damper (TMD) is one of the most used strategies in high-rise buildings to reduce the vibrations caused by wind forces. Nonetheless, there are many practical challenges to overcome, such as whether the actual performance of a TMD matches its initial design. This requires verification through performing actual testing. However, conducting actual TMD vibration reduction performance testing in a laboratory setting is impractical, as it is impossible to construct the real building with the full-scale TMD in the wind tunnel or on the shaking table. Consequently, most TMDs do not undergo actual performance testing before formal installation. This research uses a full-scale 100-ton TMD as an example to propose a standard testing procedure for TMDs before formal installation. One process of this proposed testing procedure of is the identification of the TMD's dynamic parameters. The dynamic parameters of the TMD are identified using both the on-site free vibration test and the shaking table identification test with system identification methods to determine the TMD's modal parameters, and the friction model of the TMD is identified using Sparse Identification of Nonlinear Dynamics (SINDy) method. The other process of this testing procedure is Real-Time Hybrid Testing with Shaking Table (RTHT-ST) to verify the TMD's control performance to reduce wind-induced vibrations in the target building. The RTHT-ST concept involves replacing the high-rise building with a numerical model while keeping the full-scale TMD on the shaking table. Through advanced control techniques of the shaking table, the interaction effects of the full-scale TMD and the high-rise building subjected to wind forces can be simulated and verified. Considering the high risk associated with RTHT-ST, this research also proposes using Hardware-in-the-Loop Simulation (HILS) as a fail-safe test for RTHT-ST. HILS can help test operators to pre-test the stability of any hardware and software in a pseudo RTHT-ST environment. Besides performance testing, this dissertation also proposes a digital twin-based Structural Health Monitoring (SHM) process for the long-term maintenance of the TMD and the target building. This process includes using the smart sensors for remote automatic vibration measurement, identifying the current dynamic characteristics of the target building and TMD through online identification analysis, estimating environmental forces applying on the target building using Kalman filter, and providing real-time 3D dynamic visualization models.

    LIST OF TABLES viii LIST OF FIGURES x CHAPTER 1 INTRODUCTION 1 1.1 Scopes and Objectives 1 1.2 Literature Review 2 1.2.1 Review of structural vibration control 2 1.2.2 Review of structural health monitoring (SHM) 7 1.2.3 Review of real-time hybrid testing (RTHT) 8 1.2.4 Review of digital twin 12 1.3 Overview 14 CHAPTER 2 HARDWARE-IN-THE-LOOP SIMULATION WITH 3D VISUALIZAION DEVELOPEMENT 17 2.1 Concept of Hardware-in-the Loop Simulation 17 2.2 Introduction to the devices & tools 18 2.2.1 Rolling Pendulum System (RPS) 18 2.2.2 Active mass damper (AMD) 19 2.2.3 Simulink 21 2.2.4 Data communication device 22 2.3 Theory 23 2.3.1 Model reference adaptive structural control (MRASC) 23 2.3.2 Recursive least square method (RLS) 28 2.4 Hardware-in-the-Loop Simulation 30 2.4.1 Framework 30 2.4.2 Mathematical derivation in HILS 31 2.4.3 MRASC parameters design 35 2.4.4 Results 37 2.5 Chapter summary 39 CHAPTER 3 INDENTIFICATION OF THE FULL-SCALE TUNED MASS DAMPER 74 3.1 Introduction to the Tuned Mass Damper 74 3.2 Theory Derivation 74 3.2.1 Dynamic equation of the TMD 74 3.2.2 Recursive least square method with adaptive forgetting factor 76 3.3 Identification Test of the TMD 78 3.3.1 Validation of the Identification Test 78 3.3.2 Introduction to the frequency adjustment of the TMD 80 3.3.3 On-site free vibration test 80 3.3.4 Shaking table test 82 3.4 Friction Identification with Sparse Identification of Nonlinear Dynamics 84 3.4.1 Sparse identification of nonlinear dynamics (SINDy) 84 3.5 SHM of DT with 3D Visualization to the TMD 88 3.6 Chapter summary 89 CHAPTER 4 REAL-TIME HYBRID TESTING – SHAKING TABLE OF FULL-SCALE TUNED MASS DAMPER 144 4.1 Concept of Real-Time Hybrid Testing with Shaking Table 144 4.2 Preliminary Theory and Information 144 4.2.1 Introduction to the primary structure 144 4.2.2 Introduction to external forces obtained through wind tunnel test 144 4.2.3 Mathematical model for RTHT-ST 146 4.3 Hardware in-the loop Simulation of High-Rise Building With TMD 149 4.3.1 Framework of HILS 149 4.3.2 Decision of input wind force in RTHT-ST 150 4.3.3 Virtual sensors 151 4.3.4 Identification of the shaking table 151 4.3.5 Phase compensation 153 4.3.6 Application of real-time 3D visualization on HILS 155 4.4 Real-time Hybrid Testing With Shaking Table 156 4.4.1 Framework of RTHT-ST 156 4.4.2 Results 156 4.5 Chapter summary 158 CHAPTER 5 STRUCTURAL HEALTH MONITORING APPLICATION: A STEP TOWARDS DIGITAL-TWIN SIMULATIONS 224 5.1 Concept of Digital Twin-Based Structural Health Monitoring Procedure 224 5.2 Structural health monitoring for the high-rise building 224 5.2.1 Covariance-driven stochastic subspace identification derivation 225 5.2.2 In-situ structural health monitoring 229 5.3 External force estimation 230 5.3.1 Inverse method based on Kalman filter 230 5.3.2 Verification of inverse method by shaking table test 235 5.4 Chapter summary 236 CHAPTER 6 CONCLUSIONS AND COMMENTS 263 REFERENCE 269

    1 Chu, S.-Y., Tt, S., & Reinhorn, A. (2005). Active, Hybrid, Semi-Active Structural Control - A Design and Implementation Handbook.
    2 Gattulli, V. (1999). Passive Energy Dissipation Systems in Structural Engineering T.T. Soong and G.F. Dargush John Wiley & Sons, Chichester. Meccanica, 34(1), 65-66. doi:10.1023/A:1004442832316
    3 Lin, C. C., Hu, C. M., Wang, J. F., & Hu, R. Y. (1994). Vibration control effectiveness of passive tuned mass dampers. Journal of the Chinese Institute of Engineers, 17(3), 367-376. doi:10.1080/02533839.1994.9677600
    4 Lin, C.C., and Wang, J.F. (2012). Optimal design and practical considerations of tuned mass dampers for structural control. Chapter in book: Design Optimization of Active and Passive Structural Control Systems, pp. 126-149, Publisher: IGI Global, 1 edition, August 31
    5 Xu, Y. L., Kwok, K. C. S., & Samali, B. (1992). Control of wind-induced tall building vibration by tuned mass dampers. Journal of Wind Engineering and Industrial Aerodynamics, 40(1), 1-32. doi:https://doi.org/10.1016/0167-6105(92)90518-F
    6 Kwok, K. C. S., & Samali, B. (1995). Performance of tuned mass dampers under wind loads. Engineering Structures, 17(9), 655-667. doi:https://doi.org/10.1016/0141-0296(95)00035-6
    7 Lackner, M. A., & Rotea, M. A. (2011). Passive structural control of offshore wind turbines. Wind Energy, 14(3), 373-388. doi:https://doi.org/10.1002/we.426
    8 Stewart, G. M., & Lackner, M. A. (2014). The impact of passive tuned mass dampers and wind–wave misalignment on offshore wind turbine loads. Engineering Structures, 73, 54-61. doi:https://doi.org/10.1016/j.engstruct.2014.04.045
    9 Lee, C.-L., Chen, Y.-T., Chung, L.-L., & Wang, Y.-P. (2006). Optimal design theories and applications of tuned mass dampers. Engineering Structures, 28(1), 43-53. doi:https://doi.org/10.1016/j.engstruct.2005.06.023
    10 Kang, Y., & Peng, L.-y. (2019). Optimisation Design and Damping Effect Analysis of Large Mass Ratio Tuned Mass Dampers. Shock and Vibration, 2019, 1-16. doi:10.1155/2019/8376781
    11 Bertollucci Colherinhas, G., Morais, M., Shzu, M., & Avila, S. (2019). Optimal Pendulum Tuned Mass Damper Design Applied to High Towers Using Genetic Algorithms: Two-DOF Modeling. International Journal of Structural Stability and Dynamics, 19. doi:10.1142/S0219455419501256
    12 Lin, C. S., Liu, F., Zhang, J., Wang, J. F., & Lin, C. C. (2019). Vibration control for serviceability enhancement of offshore platforms against environmental loadings. Smart Structures and Systems, 24(3), 403-414. https://doi.org/10.12989/sss.2019.24.3.403
    13 Wang, J.-F., Lin, G.-L., Lin, C.-C., & Jian, J.-Y. (2021). Optimum placement and design of multiple tuned mass dampers for vibration control of asymmetric buildings. Journal of Vibration and Control, 28(23-24), 3875-3889. doi:10.1177/10775463211038121
    14 Soong, T. T., & Reinhorn, A. M. (1993). An overview of active and hybrid structural control research in the U.S. The Structural Design of Tall Buildings, 2(3), 193-209. doi:https://doi.org/10.1002/tal.4320020303
    15 Soong, T. T., & Spencer, B. F. (2002). Supplemental energy dissipation: state-of-the-art and state-of-the-practice. Engineering Structures, 24(3), 243-259. doi:https://doi.org/10.1016/S0141-0296(01)00092-X
    16 Chung, L. L., Lin, C. C., & Chu, S. Y. (1993). Optimal Direct Output Feedback of Structural Control. Journal of Engineering Mechanics, 119(11), 2157-2173. doi:10.1061/(ASCE)0733-9399(1993)119:11(2157)
    17 Agrawal, A. K., Fujino, Y., & Bhartia, B. K. (1993). Instability due to time delay and its compensation in active control of structures. Earthquake Engineering & Structural Dynamics, 22(3), 211-224. doi:https://doi.org/10.1002/eqe.4290220304
    18 Inaudi, J. A., & Kelly, J. M. (1994). A robust delay-compensation technique based on memory. Earthquake Engineering & Structural Dynamics, 23(9), 987-1001. doi:https://doi.org/10.1002/eqe.4290230905
    19 Agrawal, A. K., & Yang, J. N. (1997). Effect of fixed time delay on stability and performance of actively controlled civil engineering structures. Earthquake Engineering & Structural Dynamics, 26(11), 1169-1185. doi:https://doi.org/10.1002/(SICI)1096-9845(199711)26:11<1169::AID-EQE702>3.0.CO;2-S
    20 Lin, C. C., Sheu, J. F., Chu, S. Y., & Chung, L. L. (1996). TIME-DELAY EFFECT AND ITS SOLUTION FOR OPTIMAL OUTPUT FEEDBACK CONTROL OF STRUCTURES. Earthquake Engineering & Structural Dynamics, 25(6), 547-559. doi:https://doi.org/10.1002/(SICI)1096-9845(199606)25:6<547::AID-EQE566>3.0.CO;2-#
    21 Chu, S. Y., Soong, T. T., Lin, C. C., & Chen, Y. Z. (2002). Time-delay effect and compensation on direct output feedback controlled mass damper systems. Earthquake Engineering & Structural Dynamics, 31(1), 121-137. doi:https://doi.org/10.1002/eqe.101
    22 Chu, S.-Y., Lin, C.-C., Chung, L.-L., Chang, C.-C., & Lu, K.-H. (2008). Optimal performance of discrete-time direct output-feedback structural control with delayed control forces. Structural Control and Health Monitoring, 15(1), 20-42. doi:https://doi.org/10.1002/stc.193
    23 Chu, S.-Y., Lo, S.-C., & Chang, M.-C. (2010). Real-time control performance of a model-reference adaptive structural control system under earthquake excitation. Structural Control and Health Monitoring, 17(2), 198-217. doi:https://doi.org/10.1002/stc.287
    24 Tu, J., Lin, X., Tu, B., Xu, J., & Tan, D. (2014). Simulation and experimental tests on active mass damper control system based on Model Reference Adaptive Control algorithm. Journal of Sound and Vibration, 333(20), 4826-4842. doi:https://doi.org/10.1016/j.jsv.2014.05.043
    25 Abdel-Rohman, M. (1984). Optimal design of active TMD for buildings control. Building and Environment, 19(3), 191-195. doi:https://doi.org/10.1016/0360-1323(84)90026-X
    26 Facioni, R. J., Kwok, K. C. S., & Samali, B. (1995). Wind tunnel investigation of active vibration control of tall buildings. Journal of Wind Engineering and Industrial Aerodynamics, 54-55, 397-412. doi:https://doi.org/10.1016/0167-6105(94)00056-J
    27 Wu, J.-C., & Pan, B.-C. (2002). Wind tunnel verification of actively controlled high-rise building in along-wind motion. Journal of Wind Engineering and Industrial Aerodynamics, 90(12), 1933-1950. doi:https://doi.org/10.1016/S0167-6105(02)00299-4
    28 Cao, H., Reinhorn, A. M., & Soong, T. T. (1998). Design of an active mass damper for a tall TV tower in Nanjing, China. Engineering Structures, 20(3), 134-143. doi:https://doi.org/10.1016/S0141-0296(97)00072-2
    29 Lu, X., Li, P., Guo, X., Shi, W., & Liu, J. (2014). Vibration control using ATMD and site measurements on the Shanghai World Financial Center Tower. The Structural Design of Tall and Special Buildings, 23(2), 105-123. doi:https://doi.org/10.1002/tal.1027
    30 Zhou, K., & Li, Q.-S. (2022). Vibration mitigation performance of active tuned mass damper in a super high-rise building during multiple tropical storms. Engineering Structures, 269, 114840. doi:https://doi.org/10.1016/j.engstruct.2022.114840
    31 Spencer, B. F., & Nagarajaiah, S. (2003). State of the Art of Structural Control. Journal of Structural Engineering, 129(7), 845-856. doi:10.1061/(ASCE)0733-9445(2003)129:7(845)
    32 Chu, S.-Y., Yeh, S.-W., Lu, L.-Y., & Peng, C.-H. (2017a). Experimental verification of leverage-type stiffness-controllable tuned mass damper using direct output feedback LQR control with time-delay compensation. Earthquake and Structures, 12, 425-436. doi:10.12989/eas.2017.12.4.425
    33 Chu, S.-Y., Yeh, S.-W., Lu, L.-Y., & Peng, C.-H. (2017b). A leverage-type stiffness controllable mass damper for vibration mitigation of structures. Structural Control and Health Monitoring, 24(4), e1896. doi:https://doi.org/10.1002/stc.1896
    34 Varadarajan, N., & Nagarajaiah, S. (2004). Wind Response Control of Building with Variable Stiffness Tuned Mass Damper Using Empirical Mode Decomposition/Hilbert Transform. Journal of Engineering Mechanics, 130(4), 451-458. doi:10.1061/(ASCE)0733-9399(2004)130:4(451)
    35 Kang, J., Kim, H.-S., & Lee, D.-G. (2011). Mitigation of wind response of a tall building using semi-active tuned mass dampers. The Structural Design of Tall and Special Buildings, 20(5), 552-565. doi:https://doi.org/10.1002/tal.609
    36 Dai, J., Xu, Z.-D., Gai, P.-P., & Xu, Y.-W. (2021). Mitigation of Vortex-Induced Vibration in Bridges Using Semiactive Tuned Mass Dampers. Journal of Bridge Engineering, 26(6), 05021003. doi:10.1061/(ASCE)BE.1943-5592.0001719
    37 Shih, M.-H., & Sung, W.-P. (2021). Seismic Resistance and Parametric Study of Building under Control of Impulsive Semi-Active Mass Damper. Applied Sciences, 11(6). doi:10.3390/app11062468
    38 Gelman, L., Petrunin, I., Parrish, C., & Walters, M. (2020). Novel health monitoring technology for in-service diagnostics of intake separation in aircraft engines. Structural Control and Health Monitoring, 27(5), e2479. doi:https://doi.org/10.1002/stc.2479
    39 Hemez, F. M., & Doebling, S. W. (2001). REVIEW AND ASSESSMENT OF MODEL UPDATING FOR NON-LINEAR, TRANSIENT DYNAMICS. Mechanical Systems and Signal Processing, 15(1), 45-74. doi:https://doi.org/10.1006/mssp.2000.1351
    40 Dessena, G., Civera, M., Zanotti Fragonara, L., Ignatyev, D. I., & Whidborne, J. F. (2023). A Loewner-Based System Identification and Structural Health Monitoring Approach for Mechanical Systems. Structural Control and Health Monitoring, 2023, 1891062. doi:10.1155/2023/1891062
    41 Juang, J.-N. (1994). Applied system identification. Englewood Cliffs, N.J.: Prentice Hall Englewood Cliffs, N.J.
    42 Shinozuka, M., Yun, C.-B., & Imai, H. (1982). Identification of Linear Structural Dynamic Systems. Journal of the Engineering Mechanics Division, 108(6), 1371-1390. doi:10.1061/JMCEA3.0002909
    43 Chu, S.-Y., & Lo, S.-C. (2009). Application of real-time adaptive identification technique on damage detection and structural health monitoring. Structural Control and Health Monitoring, 16(2), 154-177. doi:https://doi.org/10.1002/stc.304
    44 Chu, S.-Y., & Lo, S.-C. (2011). Application of the on-line recursive least-squares method to perform structural damage assessment. Structural Control and Health Monitoring, 18(3), 241-264. doi:https://doi.org/10.1002/stc.362
    45 Chu, S.-Y., & Kang, C.-J. (2021). Development of the structural health record of containment building in nuclear power plant. Nuclear Engineering and Technology, 53(6), 2038-2045. doi:https://doi.org/10.1016/j.net.2020.12.018
    46 Huang, S.-K., & Chi, F.-C. (2023). Development of Recursive Subspace Identification for Real-Time Structural Health Monitoring under Seismic Loading. Structural Control and Health Monitoring, 2023, 1117042. doi:10.1155/2023/1117042
    47 Wang, Jer-Fu, & Lin, Chi-Chang. (2015). Extracting parameters of TMD and primary structure from the combined system responses. Smart Structures and Systems, 16(5), 937–960. https://doi.org/10.12989/SSS.2015.16.5.937
    48 Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016a). Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proceedings of the National Academy of Sciences, 113(15), 3932-3937. doi:10.1073/pnas.1517384113
    49 Brunton, S. L., & Kutz, J. N. (2022). Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control (2 ed.). Cambridge: Cambridge University Press.
    50 Cortiella, A., Park, K.-C., & Doostan, A. (2021). Sparse identification of nonlinear dynamical systems via reweighted ℓ1-regularized least squares. Computer Methods in Applied Mechanics and Engineering, 376, 113620. doi:https://doi.org/10.1016/j.cma.2020.113620
    51 Brunton, S. L., Proctor, J. L., & Kutz, J. N. (2016b). Sparse Identification of Nonlinear Dynamics with Control (SINDYc)**SLB acknowledges support from the U.S. Air Force Center of Excellence on Nature Inspired Flight Technologies and Ideas (FA9550-14-1-0398). JLP thanks Bill and Melinda Gates for their active support of the Institute of Disease Modeling and their sponsorship through the Global Good Fund. JNK acknowledges support from the U.S. Air Force Office of Scientific Research (FA9550-09-0174). IFAC-PapersOnLine, 49(18), 710-715. doi:https://doi.org/10.1016/j.ifacol.2016.10.249
    52 Hakuno, M., Shidawara, M., & Hara, T. (1969). DYNAMIC DESTRUCTIVE TEST OF A CANTILEVER BEAM, CONTROLLED BY AN ANALOG-COMPUTER. Proceedings of the Japan Society of Civil Engineers, 1969(171), 1-9. doi:10.2208/jscej1969.1969.171_1
    53 Chung, W.-J., Yun, C.-B., Kim, N.-S., & Seo, J.-W. (1999). Shaking table and pseudodynamic tests for the evaluation of the seismic performance of base-isolated structures. Engineering Structures, 21(4), 365-379. doi:https://doi.org/10.1016/S0141-0296(97)00211-3
    54 Reinhorn, A.M., Bruneau M., Chu, S.-Y, Shao X., and Pitman. M.C. (2003). Large scale real time dynamic hybrid testing technique -shake tables substructure testing. 2003 ASCE/SEI Structures Congress and Exposition: Engineering Smarter, May 29, 2003 - May 31, 2003. Seattle, WA, United states: American Society of Civil Engineers, 67-72.
    55 Lee, S.-K., Park, E. C., Min, K.-W., & Park, J.-H. (2007). Real-time substructuring technique for the shaking table test of upper substructures. Engineering Structures, 29(9), 2219-2232. doi:https://doi.org/10.1016/j.engstruct.2006.11.013
    56 Sorace, S., Terenzi, G., Magonette, G., & Molina, F. J. (2008). Experimental investigation on a base isolation system incorporating steel–Teflon sliders and pressurized fluid viscous spring dampers. Earthquake Engineering & Structural Dynamics, 37(2), 225-242. doi:https://doi.org/10.1002/eqe.753
    57 Chu, S. Y., Soong, T. T., Reinhorn, A. M., Helgeson, R. J., & Riley, M. A. (2002). Integration issues in implementation of structural control systems. Journal of Structural Control, 9(1), 31-58. doi:https://doi.org/10.1002/stc.2
    58 Chu, S.-Y., Soong, T. T., & Reinhorn, A. M. (2002). Real-time active control verification via a structural simulator. Engineering Structures, 24(3), 343-353. doi:https://doi.org/10.1016/S0141-0296(01)00100-6
    59 Chen, P.-C., Tsai, K.-C., & Lin, P.-Y. (2014). Real-time hybrid testing of a smart base isolation system. Earthquake Engineering & Structural Dynamics, 43(1), 139-158. doi:https://doi.org/10.1002/eqe.2341
    60 Chen, P.-C., & Chen, P.-C. (2023). Real-time hybrid simulation for seismic control performance evaluation of an active inerter damper system. Engineering Structures, 294, 116760. doi:https://doi.org/10.1016/j.engstruct.2023.116760
    61 Liu, J., Silva, C. E., Dyke, S. J., Wu, Y., & Liu, H. (2024). Using real-time hybrid simulation for active mass damper experimentation and validation. Mechanism and Machine Theory, 191, 105474. doi:https://doi.org/10.1016/j.mechmachtheory.2023.105474
    62 Chu, S.-Y., Lu, L.-Y., & Yeh, S.-W. (2018). Real-time hybrid testing of a structure with a piezoelectric friction controllable mass damper by using a shake table. Structural Control and Health Monitoring, 25(3), e2124. doi:https://doi.org/10.1002/stc.2124
    63 Wang, J.-T., Gui, Y., Zhu, F., Jin, F., & Zhou, M.-X. (2016). Real-time hybrid simulation of multi-story structures installed with tuned liquid damper. Structural Control and Health Monitoring, 23(7), 1015-1031. doi:https://doi.org/10.1002/stc.1822
    64 Zhu, F., Wang, J.-T., Jin, F., & Lu, L.-Q. (2017). Real-time hybrid simulation of full-scale tuned liquid column dampers to control multi-order modal responses of structures. Engineering Structures, 138, 74-90. doi:https://doi.org/10.1016/j.engstruct.2017.02.004
    65 Ozdagli, A. I., Xi, W., Ou, G., Li, B., Dyke, S. J., Wu, B., . . . Wang, T. (2020). Experimental verification of an accessible geographically distributed real-time hybrid simulation platform. Structural Control and Health Monitoring, 27(2), e2483. doi:https://doi.org/10.1002/stc.2483
    66 Li, X., Ozdagli Ali, I., Dyke Shirley, J., Lu, X., & Christenson, R. (2017). Development and Verification of Distributed Real-Time Hybrid Simulation Methods. Journal of Computing in Civil Engineering, 31(4), 04017014. doi:10.1061/(ASCE)CP.1943-5487.0000654
    67 Ou, G., Ozdagli, A. I., Dyke, S. J., & Wu, B. (2015). Robust integrated actuator control: experimental verification and real-time hybrid-simulation implementation. Earthquake Engineering & Structural Dynamics, 44(3), 441-460. doi:https://doi.org/10.1002/eqe.2479
    68 Stöppler, G., Menzel, T., & Douglas, S. (2005). Hardware-in-the-loop simulation of machine tools and manufacturing systems. Computing & Control Engineering Journal, 16, 10-15. doi:10.1049/ccej:20050101
    69 Lin, C.-F., Tseng, C.-Y., & Tseng, T.-W. (2006). A hardware-in-the-loop dynamics simulator for motorcycle rapid controller prototyping. Control Engineering Practice, 14(12), 1467-1476. doi:https://doi.org/10.1016/j.conengprac.2005.12.001
    70 Xu, L. f., Li, J. q., Hua, J. f., Li, X. j., & Ouyang, M. g. (2009, 7-10 Sept. 2009). Hardware in the loop simulation of vehicle controller unit for fuel cell/battery hybrid bus. Paper presented at the 2009 IEEE Vehicle Power and Propulsion Conference.
    71 Rosli, R., Mailah, M., & Priyandoko, G. (2013). Hardware-in-the-Loop Simulation for Active Force Control with Iterative Learning Applied to an Active Vehicle Suspension System. Applied Mechanics and Materials, 465-466. doi:10.4028/www.scientific.net/AMM.465-466.801
    72 G, K., M, P., M, M., & H. I, B. (2018, 25-27 Oct. 2018). Hardware-In-the-Loop Simulation for Semi-Active Suspension System with Using Adaptive Backstepping Approach. Paper presented at the 2018 6th International Conference on Control Engineering & Information Technology (CEIT).
    73 Kwak, M. K., Lee, J.-H., Yang, D.-H., & You, W.-H. (2014). Hardware-in-the-loop simulation experiment for semi-active vibration control of lateral vibrations of railway vehicle by magneto-rheological fluid damper. Vehicle System Dynamics, 52(7), 891-908. doi:10.1080/00423114.2014.906631
    74 Zhang, Y., Schauer, T., Wernicke, L., Vrontos, A., Engelmann, M., Wulff, W., & Bleicher, A. (2021). Design of Moveable Façade Elements for Energy Harvesting and Vibration Control of Super Slender Tall Buildings under Wind Excitation.
    75 Xu, J., Shu, X., Qiao, P., Li, S., & Xu, J. (2023). Developing a digital twin model for monitoring building structural health by combining a building information model and a real-scene 3D model. Measurement, 217, 112955. doi:https://doi.org/10.1016/j.measurement.2023.112955
    76 Wang, J., Moreira, J., Cao, Y., & Gopaluni, R. B. (2023). Simultaneous digital twin identification and signal-noise decomposition through modified generalized sparse identification of nonlinear dynamics. Computers & Chemical Engineering, 177, 108294.
    77 Jeong, S., Hou, R., Lynch, J. P., Sohn, H., & Law, K. H. (2017). An information modeling framework for bridge monitoring. Advances in Engineering Software, 114, 11-31. doi:https://doi.org/10.1016/j.advengsoft.2017.05.009
    78 Davila Delgado Juan, M., Butler Liam, J., Brilakis, I., Elshafie Mohammed, Z. E. B., & Middleton Campbell, R. (2018). Structural Performance Monitoring Using a Dynamic Data-Driven BIM Environment. Journal of Computing in Civil Engineering, 32(3), 04018009. doi:10.1061/(ASCE)CP.1943-5487.0000749
    79 O'Shea, M., & Murphy, J. (2020). Design of a BIM Integrated Structural Health Monitoring System for a Historic Offshore Lighthouse. Buildings, 10, 131. doi:10.3390/buildings10070131
    80 Branlard, E., Giardina, D., & Brown, C. S. D. (2020). Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations. Wind Energ. Sci., 5(3), 1155-1167. doi:10.5194/wes-5-1155-2020
    81 Zhao, P., Liu, L., & Lei, Y. (2022). Identification of Wind Loads on Structures Based on Modal Kalman Filter with Unknown Inputs. Buildings, 12(7). doi:10.3390/buildings12071003
    82 He, J., Deng, B., Hua, X., Zhang, X., & Yang, O. (2022). Joint Estimation of Multi-Scale Structural Responses and Unknown Loadings Based on Modal Kalman Filter Without Using Collocated Acceleration Observations. International Journal of Structural Stability and Dynamics, 22(11), 2250132. doi:10.1142/S0219455422501322
    83 Yang, B., Zhu, H., Zhang, Q., Wüchner, R., Sun, S., & Qiu, J. (2023). Identification of wind loads on a 600 m high skyscraper by Kalman filter. Journal of Building Engineering, 63, 105440. doi:https://doi.org/10.1016/j.jobe.2022.105440
    84 Hsu, C. Y. (2014). Hybrid Testing of Friction-Type Tuned Mass Damper System. National Cheng Kung University, Tainan. Retrieved from https://hdl.handle.net/11296/rqst28, National Digital Library of Theses and Dissertations in Taiwan.
    85 Hassan K. Khalil. Nonlinear systems. Macmillan Publishing Company, New York, 1992.
    86 Mei, Gang & Kareem, Ahsan & Kantor, Jeffrey. (2002). Model Predictive Control of Structures under Earthquakes using Acceleration Feedback. Journal of Engineering Mechanics-asce - J ENG MECH-ASCE. 128. 10.1061/(ASCE)0733-9399(2002)128:5(574).
    87 Chang, M. C. (2005). Model Reference Adaptive Structural Control. National Chi Nan University, Nantu. Retrieved from https://hdl.handle.net/11296/64eq2c, National Digital Library of Theses and Dissertations in Taiwan.
    88 Yeh, S. W. (2017). Verification of Real-time Hybrid Tests by Shaking Table Tests for Vibration Control Systems with Friction Property. National Cheng Kung University, Tainan. Retrieved from https://hdl.handle.net/11296/t5k6bb, National Digital Library of Theses and Dissertations in Taiwan.
    89 Bhotto, M. Z. A., & Antoniou, A. (2013). New Improved Recursive Least-Squares Adaptive-Filtering Algorithms. IEEE Transactions on Circuits and Systems I: Regular Papers, 60(6), 1548-1558. doi:10.1109/TCSI.2012.2220452
    90 Lin, J.-W., & Betti, R. (2004). On-line identification and damage detection in non-linear structural systems using a variable forgetting factor approach. Earthquake Engineering & Structural Dynamics, 33(4), 419-444. doi:https://doi.org/10.1002/eqe.350
    91 Paleologu, C., Benesty, J., & Ciochina, S. (2008). A Robust Variable Forgetting Factor Recursive Least-Squares Algorithm for System Identification. IEEE Signal Processing Letters, 15, 597-600. doi:10.1109/LSP.2008.2001559
    92 Shan, L., Chen, H., Luan, J., & Li, J. (2017, 20-22 Oct. 2017). Application of adaptive forgetting factor RLS algorithm in target tracking. Paper presented at the 2017 Chinese Automation Congress (CAC).
    93 Shu-Hung, L., & So, C. F. (2005). Gradient-based variable forgetting factor RLS algorithm in time-varying environments. IEEE Transactions on Signal Processing, 53(8), 3141-3150. doi:10.1109/TSP.2005.851110
    94 Toplis, B., & Pasupathy, S. (1988). Tracking improvements in fast RLS algorithms using a variable forgetting factor. IEEE Transactions on Acoustics, Speech, and Signal Processing, 36(2), 206-227. doi:10.1109/29.1514
    95 Sun, X., Ji, J., Ren, B., Xie, C., & Yan, D. (2019). Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery. Energies, 12(12). doi:10.3390/en12122242
    96 Mahajan, S., & Cicirello, A. (2023). Governing Equation Identification Of Nonlinear Single Degree-Of-Freedom Oscillators With Coulomb Friction Using Explicit Stick And Slip Temporal Constraints. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 9, 1-35. doi:10.1115/1.4063070
    97 Fehr, J., Kargl, A., & Eschmann, H. (2022). Identification of Friction Models for MPC-based Control of a PowerCube Serial Robot.
    98 Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267-288. doi:https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
    99 Lathourakis, C., & Cicirello, A. (2023). Physics Enhanced Sparse Identification of Dynamical Systems with Discontinuous Nonlinearities. In: Research Square.
    100 Liu, X., Li, Y., Cheng, Y., & Cai, Y. (2023). Sparse identification for ball-screw drives considering position-dependent dynamics and nonlinear friction. Robotics and Computer-Integrated Manufacturing, 81, 102486. doi:https://doi.org/10.1016/j.rcim.2022.102486
    101 Chen, M.C. (2012). Application of Stochastic Subspace Identification in Bridge Structural Health Monitoring. National Taiwan University, Taipei. Retrieved from https://hdl.handle.net/11296/u2czbf, National Digital Library of Theses and Dissertations in Taiwan.
    102 Zhi, L., Yu, P., Li, Q.-S., Chen, B., & Fang, M. (2018). Identification of wind loads on super-tall buildings by Kalman filter. Computers & Structures, 208, 105-117. doi:https://doi.org/10.1016/j.compstruc.2018.07.002
    103 Chui CK, Chen GR. Kalman filtering: with real-time applications. 4th ed. Berlin: Springer-Verlag, Berlin and Heidelberg GmbH & Co. K; 2009.
    104 Simon D. Optimal state estimation: Kalman, H infinity, and nonlinear approaches. New Jersey: John Wiley & Sons; 2006.

    下載圖示 校內:立即公開
    校外:立即公開
    QR CODE