شناسایی آسیب در سازه‌ها به کمک تحریک آشوبناک و ویژگی وابستگی متقابل بهبودیافته

نوع مقاله : پژوهشی

نویسندگان

دانشکده‌ی مهندسی عمران، دانشگاه صنعتی خواجه نصیرالدین طوسی، تهران، ایران.

چکیده

روش‌های شناسایی آسیب بر پایه‌ی تحریک آشوبناک، شامل استخراج برخی ویژگی‌ها می‌شوند که با استفاده از آن‌ها بتوان تغییرات ناشی از آسیب بر سازه را اندازه‌گیری کرد؛ که از میان آن‌ها می‌توان به ویژگی وابستگی متقابل تعمیم‌یافته اشاره کرد، که حساسیت مناسبی به آسیب، پیچیدگی محاسباتی پایین، و حساسیت نسبتاً کم به عوامل محیطی دارد. اما ویژگی مذکور محدودیت‌هایی دارد که کاربرد آن را منحصر به موارد خاص می‌کند. در پژوهش حاضر، با افزودن ضریب حساسیت به آسیب و اعمال کنترل‌هایی در نحوه‌ی عملکرد ویژگی وابستگی متقابل تعمیم‌یافته، ضمن رفع محدودیت‌های ذکرشده، از خواص مطلوب آن در شناسایی آسیب استفاده شده است. در سازه‌ی تیر یکسر گیردار بررسی‌شده، در حالتی که ویژگی وابستگی متقابل، کوچک‌ترین کاهش وابستگی را در اثر آسیب نشان نمی‌دهد، ویژگی بهبودیافته با نشان‌دادن کاهش وابستگی میان دو نقطه از سازه با بهبود عملکرد ۲۰ درصدی، در تشخیص آسیب موفق عمل کرده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Evaluation of Seismic Performance of Special Truss Moment Frames (STMF) with Vierendeel Special Segment

نویسندگان [English]

  • Alireza Bahrami
  • Saeid Asil Gharebaghi
Faculty of Civil Engineering of K. N. Toosi University of Technology, Tehran, Iran.
چکیده [English]

Damage detection methods are integral components of structural health monitoring systems. Identifying damage in structures using vibration-based methods has always been one of the most important and popular topics among researchers in structural health monitoring. Vibration-based damage identification includes extracting a feature that can be used to measure the minuscule changes caused by damage to the structure. In recent years, advances have been made in using chaotic excitation and representing damage-sensitive features based on the properties of the chaotic attractor. These types of damage-sensitive features try to measure the minuscule changes caused by structural damage by comparing the chaotic attractors obtained from the structural response. The high sensitivity of chaotic systems to small changes makes attractor-based features suitable for identifying structural damage. One of the most widely used attractor-based features is the Generalized Interdependence, which has a reasonable sensitivity to damage and relatively low computational complexity. Also, the comparative nature of this feature can help identify damage in the presence of environmental variables such as noise. However, this feature has limitations that make its use exclusive to particular instances. e.g., in structures where the exact location of the damage is known beforehand. In the damage identification method presented in this research, improvements like adding a damage sensitivity factor and applying controls over the operation have been made to this feature to remove these limitations while preserving its exceptional properties in detecting damage in structures. In the structure examined in this research, where the generalized interdependence feature does not show the slightest decrease in dependence due to damage, the improved feature detects damage by showing about 20% better performance in finding a reduction in the dependence between two points of the structure. Two points of the structure are selected to be located at different distances from the damage. In other words, the improved feature can measure the different impacts due to damage on these two points.
 

کلیدواژه‌ها [English]

  • Damage detection
  • generalized interdependence
  • chaotic attractor
  • chaotic excitation

مراجع

1. Fan, W. and Qiao, P., 2011. Vibration-based damage identification methods: a review and comparative study. Structural Health Monitoring, 10, pp. 83-111. . http://dx.doi.org/10.1177/1475921710365419
Doebling, S. W., Farrar, C. R. and Prime, M. B., 1998. A summary review of vibration-based damage identification methods. Shock and Vibration Digest, 30, pp. 91-105. . http://dx.doi.org/10.1177/058310249803000201
2. Sohn, H., Farrar, C. R., Hemez, F. M., Shunk, D. D., Stinemates, D. W., Nadler, B. R. and Czarnecki, J. J., 2003. A review of structural health monitoring literature: 1996–2001. Los Alamos National Laboratory, USA, 1, pp. 16. . https://www.osti.gov/servlets/purl/976152.
3. Das, S., Saha, P. and Patro, S., 2016. Vibration-based damage detection techniques used for health monitoring of structures: a review. Journal of Civil Structural Health Monitoring, 6, pp. 477-507. https://doi.org/10.1007/s13349-016-0168-5
4. Nikkhoo, A., Karegar, H., Mohammadi, R. K. and Hejazi, F., 2020. Improving the performance of the autoregressive method in modal identification of output-only systems using the empirical mode decomposition. Structures, pp. 1165-1173. Elsevier. http://dx.doi.org/10.1016/j.istruc.2020.07.006
5. Nikkhoo, A., Karegar, H. and Karami Mohammadi, R., 2021. Improving the performances of the autoregressive method in modal identification of output-only systems using Hilbert vibration decomposition method. Sharif Journal of Civil Engineering, 37.2, pp. 19-28 [In Persian]. https://doi.org/10.24200/j30.2020.55163.2711
6. Nikkhoo, A., Shemshaki, E. and Karegar, H., 2024. Reliability evaluation of stochastic subspace identification and frequency domain decomposition methods in estimating modal parameters of a structure excited by the earthquake. Sharif Journal of Civil Engineering, 39, pp. 111-121 [In Persian]. https://doi.org/10.24200/j30.2023.61746.3195
7. Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M. and Inman, D. J., 2021. A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications. Mechanical Systems and Signal Processing, 147, pp. 077-107. . https://doi.org/10.1016/j.ymssp.2020.107077
8. Sun, X., Ilanko, S., Mochida, Y. and Tighe, R. C., 2023. A Review on Vibration-Based Damage Detection Methods for Civil Structures. Vibration, 6, pp. 843-875. . http://dx.doi.org/10.3390/vibration6040051
9. Nichols, J., Trickey, S., Todd, M. and Virgin, L., 2003. Structural health monitoring through chaotic interrogation. Meccanica, 38, pp. 239-250. https://doi.org/10.1023/A:1022898403359
10. Nichols, J., Todd, M., Seaver, M. and Virgin, L., 2003. Use of chaotic excitation and attractor property analysis in structural health monitoring. Physical Review E, 67, 016209. http://dx.doi.org/10.1103/PhysRevE.67.016209
11. Nichols, J., Nichols, C., Todd, M., Seaver, M., Trickey, S. and Virgin, L. 2004., Use of data-driven phase space models in assessing the strength of a bolted connection in a composite beam. Smart Materials and Structures, 13, 241. http://dx.doi.org/10.1088/0964-1726/13/2/001
12. Overbey, L. and Todd, M., 2008. Damage assessment using generalized state-space correlation features. Structural Health Monitoring, 7, pp. 347-363. http://dx.doi.org/10.1177/1475921708090568
13. Torkamani, S., Butcher, E. A., Todd, M. D. and Park, G., 2010. Damage assessment using hyperchaotic excitation and state-space geometry changes. Smart Materials, Adaptive Structures and Intelligent Systems, pp. 599-608. https://doi.org/10.1115/SMASIS2010-3705
14. Torkamani, S., Butcher, E., Todd, M. and Park, G., 2011. Detection of system changes due to damage using a tuned hyperchaotic probe. Smart Materials and Structures, 20, 025006. http://dx.doi.org/10.1088/0964-1726/20/2/025006
15. Torkamani, S., Butcher, E. A. and Todd, M. D., 2016. A real-time approach for damage identification using hyperchaotic probe and stochastic estimation. Meccanica, 51, pp. 537-550. https://doi.org/10.1007/s11012-015-0211-3
16. Sloboda, A. R. and Kong, C. T. 2022., Boundary transformation vectors: a geometric method of quantifying attractor deformation for structural health monitoring. Journal of Computational and Nonlinear Dynamics, 17, 121004. . https://doi.org/10.1115/1.4055791
17. Takens, T., 1981. Detecting strange attractors in turbulence. Lecture Notes in Math., 898, pp. 336-381. http://dx.doi.org/10.1007/BFb0091924
18. Kostelich, E. J. and Schreiber, T., 1993. Noise reduction in chaotic time-series data: a survey of common methods. Physical Review E, 48, 1752. https://doi.org/10.1103/PhysRevE.48.1752
19. Fraser, A. M. and Swinney, H. L., 1986. Independent coordinates for strange attractors from mutual information. Physical Review A, 33, 1134. https://doi.org/10.1103/PhysRevA.33.1134
20. Todd, M., Nichols, J., Olson, C. and Overbey, L., 2005. Detecting generalized dynamic inter-relationship in a frame experiment with measures of information flow and interdependence. Health Monitoring and Smart Nondestructive Evaluation of Structural and Biological Systems IV, pp. 264-273. SPIE. https://doi.org/10.1177/1475921708090568
21. Arnhold, J., Grassberger, P., Lehnertz, K. and Elger, C. E., 1999. A robust method for detecting interdependences: application to intracranially recorded EEG. Physica D: Nonlinear Phenomena, 134, pp. 419-430. https://doi.org/10.1016/S0167-2789(99)00140-2
22. Theiler, J., 1986. Spurious dimension from correlation algorithms applied to limited time-series data. Physical Review A, 34, 2427. https://doi.org/10.1103/PhysRevA.34.2427
23. Meirovitch, L., 1997. Principles and techniques of vibrations. (No Title).
24. Wolf, A., Swift, J. B., Swinney, H. L. and Vastano, J. A., 1985. Determining lyapunov exponents from a time series. Physica D: Nonlinear Phenomena, 16, pp. 285-317. https://doi.org/10.1016/0167-2789(85)90011-9
25. Pecora, L. M. and Carroll, T. L., 1996. Discontinuous and nondifferentiable functions and dimension increase induced by filtering chaotic data. Chaos: An Interdisciplinary Journal of Nonlinear Science, 6, pp. 432-439. https://doi.org/10.1063/1.166186 
 

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