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شناسایی پارامترهای سازه‌ای تراز جداساز پایه بر اساس روش زیرفضا | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 12، دوره 53، شماره 12، اسفند 1400، صفحه 5307-5324 اصل مقاله (2.4 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2020.18784.6961 | ||
| نویسندگان | ||
| کاوه کرمی* 1؛ پژمان فاتحی2؛ اسرا حسینی2 | ||
| 1استادیار گروه عمران دانشکده مهندسی دانشگاه کردستان | ||
| 2گروه مهندسی عمران، دانشکده مهندسی، دانشگاه کردستان | ||
| چکیده | ||
| یکی از روش‌های متداول در کنترل پاسخ لرزه‌ای سازه‌ها استفاده از جداساز لرزه‌ای می‌باشد. جداسازها با افزایش زمان تناوب سازه، برش پایه و همچنین جابه‌جایی نسبی طبقات را کاهش می‌دهند. به طور معمول، در اثر عوامل محیطی شدید مانند زلزله و باد تغییر شکل زیادی در تراز جداساز اتفاق می‌افتد که این امر می‌تواند منجر به وقوع آسیب در جداسازها شود؛ که در اثر آن احتمال ایجاد جابه‌جایی ماندگار در تراز جداساز و همچنین برخورد سازه با ساختمان‌های مجاور وجود دارد. از این رو برای جلوگیری از وقوع خسارت در ساختمان‌های مجهز به جداساز تحت اثر تحریکات شدید زمین، شناسایی آسیب در تراز جداساز از اهمیت زیادی برخوردار است. در این پژوهش، با فرض رفتار خطی سازه اصلی یک روش پیشنهادی بر مبنای زیرفضا برای شناسایی سختی تراز جداساز با تعداد حسگرهای محدود ارائه می‌گردد. برای این منظور با استفاده از تکنیک فشرده‌سازی، سازه مجهز به جداساز با تعداد درجات آزادی زیاد به یک سازه دو درجه آزاد تبدیل می‌گردد؛ به طوری که سختی مرتبط با اولین درجه آزادی در سیستم کاهش یافته متناظر با سختی تراز جداساز در سازه اولیه باشد. سپس با استفاده از پارامترهای مارکوف شناسایی شده سیستم، سختی سازه کاهش یافته شناسایی می‌گردد. به منظور ارزیابی و مقایسه عملکرد روش پیشنهادی از مثال‌های عددی استفاده می‌شود. نتایج به دست آمده نشان می‌دهد که حتی با وجود اغتشاش در پاسخ‌های اندازه‌گیری شده، روش پیشنهادی مقدار آسیب در تراز جداساز را با دقت قابل قبولی شناسایی می‌نماید. | ||
| کلیدواژه‌ها | ||
| جداساز لرزه‌ای؛ شناسایی آسیب؛ روش زیر فضا؛ کنترل غیرفعال؛ نظارت سلامت سازه‌ای | ||
| موضوعات | ||
| پایش سلامت سازه ها؛ دینامیک سازه؛ کشف عیوب؛ کنترل سازه | ||
| عنوان مقاله [English] | ||
| Subspace based identification of structural parameters of the base isolation level | ||
| نویسندگان [English] | ||
| Kaveh Karami1؛ Pejman Fatehi2؛ Asra Hoseini2 | ||
| 1Assistant Professor of Structural Engineering. Department of Civil Engineering, University of Kurdistan | ||
| 2Department of Civil Engineering, University of Kurdistan, Sanandaj, Iran | ||
| چکیده [English] | ||
| One of the common methods in controlling the seismic response of structures is the use of seismic isolators. Base isolations reduce the base shear as well as the relative displacement of the floors by increasing the period of the structure. Typically, extreme deformation of the base isolation level occurs due to severe environmental factors, which can lead to damage to the base isolations; As a result, there is a possibility of permanent deformation in the base isolation and also the collision of the structure with adjacent buildings. Therefore, to prevent damage to buildings equipped with base isolations due to severe ground motions, it is important to identify damage at the base isolations. In this study, assuming the linear behavior of the main structure, a proposed subspace-based method for identifying the stiffness of the base isolation with a limited number of sensors is presented. For this purpose, using the compression technique, the structure equipped with a separator with a large number of degrees of freedom (DOFs) is transformed into a two DOF structure; So that the stiffness associated with the first DOF in the reduced system corresponds to the stiffness of the Base isolation level in the original structure. Then, using the identified Markov parameters of the system, the reduced structural stiffness is identified. Numerical examples are used to evaluate and compare the performance of the proposed method. The results show that even in the presence of noises in the measured responses, the proposed method detects the amount of damage at the base isolation level with acceptable accuracy. | ||
| کلیدواژه‌ها [English] | ||
| Base isolation, Damage identification, Subspace technique, Passive control, Structural health monitoring | ||
| مراجع | ||
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[1] F. Amini, K. Karami, Capacity design by developed pole placement structural control, Structural Engineering and Mechanics, 39(1) (2011) 147-168. [2] K. Karami, F. Amini, Decreasing the damage in smart structures using integrated online DDA/ISMP and semi-active control, Smart Materials and Structures, 21(10) (2012) 105017. [3] K. Karami, S. Nagarajaiah, F. Amini, Developing a smart structure using integrated DDA/ISMP and semi-active variable stiffness device, SMART STRUCTURES AND SYSTEMS, 18(5) (2016) 955-982. [4] K. Karami, S. Manie, K. Ghafouri, S. Nagarajaiah, Nonlinear structural control using integrated DDA/ISMP and semi-active tuned mass damper, Engineering Structures, 181 (2019) 589-604. [5] B. Basu, O.S. Bursi, F. Casciati, S. Casciati, A.E. Del Grosso, M. Domaneschi, L. Faravelli, J. Holnicki‐Szulc, H. Irschik, M. Krommer, A European Association for the Control of Structures joint perspective. Recent studies in civil structural control across Europe, Structural Control and Health Monitoring, 21(12) (2014) 1414-1436. [6] P. Clemente, Seismic isolation: past, present and the importance of SHM for the future, Journal of Civil Structural Health Monitoring, 7(2) (2017) 217-231. [7] F. Zhou, P. Tan, Recent progress and application on seismic isolation energy dissipation and control for structures in China, Earthquake Engineering and Engineering Vibration, 17(1) (2018) 19-27. [8] M.G. Soto, H. Adeli, Vibration control of smart base-isolated irregular buildings using neural dynamic optimization model and replicator dynamics, Engineering Structures, 156 (2018) 322-336. [9] T.A. Rawlinson, J.D. Marshall, K.L. Ryan, H. Zargar, Development and experimental evaluation of a passive gap damper device to prevent pounding in base‐isolated structures, Earthquake Engineering & Structural Dynamics, 44(11) (2015) 1661-1675. [10] E.A. Mavronicola, P.C. Polycarpou, P. Komodromos, Spatial seismic modeling of base‐isolated buildings pounding against moat walls: effects of ground motion directionality and mass eccentricity, Earthquake Engineering & Structural Dynamics, 46(7) (2017) 1161-1179. [11] F. Amini, K. Karami, Damage detection algorithm based on identified system Markov parameters (DDA/ISMP) in building structures with limited sensors, Smart Materials and Structures, 21(5) (2012) 055010. [12] M. Nigro, S.N. Pakzad, S. Dorvash, Localized structural damage detection: a change point analysis, Computer‐Aided Civil and Infrastructure Engineering, 29(6) (2014) 416-432. [13] H. Qarib, H. Adeli, Recent advances in health monitoring of civil structures, Scientia Iranica, 21(6) (2014) 1733-1742. [14] K. Karami, S. Akbarabadi, Developing a Smart Structure Using Integrated Subspace‐Based Damage Detection and Semi‐Active Control, Computer‐Aided Civil and Infrastructure Engineering, 31(11) (2016) 887-903. [15] S. Manie, K. Karami, P. Fatehi, Real time system identification in smart structures using wavelet transform based sparse component analysis, Amirkabir Journal of Civil Engineering, (2019) -. [16] K. Karami, P. Fatehi, A. Yazdani, On‐line system identification of structures using wavelet‐Hilbert transform and sparse component analysis, Computer‐Aided Civil and Infrastructure Engineering, (2020). [17] T.N. Trinh, C.G. Koh, An improved substructural identification strategy for large structural systems, Structural Control and Health Monitoring, 19(8) (2012) 686-700. [18] C.G. Koh, L.M. See, T. Balendra, Estimation of structural parameters in time domain: a substructure approach, Earthquake Engineering & Structural Dynamics, 20(8) (1991) 787-801. [19] C. Koh, B. Hong, C. Liaw, Substructural and progressive structural identification methods, Engineering structures, 25(12) (2003) 1551-1563. [20] K. Tee, C. Koh, S. Quek, Substructural first‐and second‐order model identification for structural damage assessment, Earthquake Engineering & Structural Dynamics, 34(15) (2005) 1755-1775. [21] R. Li, L. Zhou, J.N. Yang, Experimental verifications of a structural damage identification technique using reduced order finite-element model, in: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2010, International Society for Optics and Photonics, 2010, pp. 76470A. [22] S. Law, D. Yong, Substructure methods for structural condition assessment, Journal of Sound and Vibration, 330(15) (2011) 3606-3619. [23] S. Weng, Y. Xia, Y.-L. Xu, H.-P. Zhu, Substructure based approach to finite element model updating, Computers & structures, 89(9-10) (2011) 772-782. [24] R. Yoshimoto, A. Mita, K. Okada, Damage detection of base‐isolated buildings using multi‐input multi‐output subspace identification, Earthquake engineering & structural dynamics, 34(3) (2005) 307-324. [25] G.J. Yun, K.A. Ogorzalek, S.J. Dyke, W. Song, A parameter subset selection method using residual force vector for detecting multiple damage locations, Structural Control and Health Monitoring: The Official Journal of the International Association for Structural Control and Monitoring and of the European Association for the Control of Structures, 17(1) (2010) 48-67. [26] H. Fang, T.J. Wang, X. Chen, Model updating of lattice structures: a substructure energy approach, Mechanical systems and signal processing, 25(5) (2011) 1469-1484. [27] J. Li, S. Law, Y. Ding, Substructure damage identification based on response reconstruction in frequency domain and model updating, Engineering Structures, 41 (2012) 270-284. [28] G. Zhang, B. Tang, G. Tang, An improved stochastic subspace identification for operational modal analysis, Measurement, 45(5) (2012) 1246-1256. [29] A.L. Hong, F. Ubertini, R. Betti, New stochastic subspace approach for system identification and its application to long-span bridges, Journal of Engineering Mechanics, 139(6) (2013) 724-736. [30] L. Yan, A. Elgamal, G.W. Cottrell, Substructure vibration NARX neural network approach for statistical damage inference, Journal of Engineering Mechanics, 139(6) (2013) 737-747. [31] J. Hou, Ł. Jankowski, J. Ou, An online substructure identification method for local structural health monitoring, Smart materials and structures, 22(9) (2013) 095017. [32] S. Law, K. Zhang, Z. Duan, Structural damage detection from coupling forces between substructures under support excitation, Engineering Structures, 32(8) (2010) 2221-2228. [33] J. Hou, Ł. Jankowski, J. Ou, A substructure isolation method for local structural health monitoring, Structural Control and Health Monitoring, 18(6) (2011) 601-618. [34] D. Zhang, E.A. Johnson, Substructure identification for shear structures: cross-power spectral density method, Smart Materials and Structures, 21(5) (2012) 055006. [35] D. Zhang, E.A. Johnson, Substructure identification for shear structures I: Substructure identification method, Structural Control and Health Monitoring, 20(5) (2013) 804-820. [36] D. Zhang, E.A. Johnson, Substructure identification for shear structures II: Controlled substructure identification, Structural Control and Health Monitoring, 20(5) (2013) 821-834. [37] D. Zhang, E.A. Johnson, Controlled loop substructure identification for shear structures, Structural Control and Health Monitoring, 21(6) (2014) 979-995. [38] D. Zhang, Y. Yang, T. Wang, H. Li, Improving substructure identification accuracy of shear structures using virtual control system, Smart Materials and Structures, 27(2) (2018) 025013. [39] P.I. Komodromos, Seismic isolation for earthquake-resistant structures, Wit Press, 2000. [40] C.-S. Lin, D.-Y. Chiang, Modal identification from nonstationary ambient response data using extended random decrement algorithm, Computers & Structures, 119 (2013) 104-114. [41] J.-N. Juang, M. Phan, L.G. Horta, R.W. Longman, Identification of observer/Kalman filter Markov parameters-Theory and experiments, Journal of Guidance, Control, and Dynamics, 16(2) (1993) 320-329. [42] S. Narasimhan, S. Nagarajaiah, E.A. Johnson, H.P. Gavin, Smart base‐isolated benchmark building. Part I: problem definition, Structural Control and Health Monitoring: The Official Journal of the International Association for Structural Control and Monitoring and of the European Association for the Control of Structures, 13(2‐3) (2006) 573-588. [43] F. Amini, S.A. Mohajeri, M. Javanbakht, Semi-active control of isolated and damaged structures using online damage detection, Smart Materials and Structures, 24(10) (2015) 105002. | ||
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