ارزیابی تأثیر میراگر جرمی تنظیم شونده بر عملکرد لرزه‌ای سازه فولادی جداسازی شده ا استفاده از تحلیل زمان دوام

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی عمران، دانشکده فنی ومهندسی، دانشگاه محقق اردبیلی، اردبیل، ایران

چکیده

جداسازها و میراگرها، ابزارهای اتلاف انرژی برای کنترل و کاهش پاسخ‌های لرزه‌ای سازه‌ها تحت زمین‌لرزه‌های شدید محسوب می‌شوند. جداساز هسته سربی (LRB) و میراگر جرمی تنظیم شونده (TMD) دو نمونه از متداولترین این ابزارها هستند. مطالعه‌ رفتار غیرخطی و خواص لرزه‌ای سیستم‌های سازه‌ای مجهز به این نوع جداسازها و میراگرها می‌تواند کمک قابل‌توجهی به درک رفتار این سیستم‌ها در برابر نیروهای جانبی ناشی از زمین‌لرزه‌های بزرگ نماید. در این پژوهش سازه فولادی 9 طبقه طراحی شده، با استفاده از روش زمان دوام در حالت‌های با پایه ثابت و جداسازی شده به همراه میراگر با نسبت‌های جرمی مختلف و بدون میراگر جرمی در نرم‌افزار SAP2000 تحلیل شده است. نتایج تحلیل‌های انجام‌گرفته نشان می‌دهد که وجود میراگر جرمی تنظیم شونده در کاهش میزان جابه‌جایی تراز جداساز، نسبت جابه‌جایی نسبی و نیروی برش طبقات بسیار مؤثر هست که با افزایش نسبت جرمی میراگر، این اثر بیشتر می‌گردد. ولی در کاهش پاسخ شتاب مطلق سازه مؤثر نبوده و با افزایش نسبت جرمی میراگر جرمی از کارایی سامانه­ کنترلی در بهبود پاسخ شتاب نسبت به حالت جداساز تنها کاسته می‌شود.

کلیدواژه‌ها


عنوان مقاله [English]

Assessment of the effect of the tuned mass damper on the seismic performance of isolated steel structures by using endurance time analysis

نویسندگان [English]

  • Milad Zarbilinezhad
  • Amin Gholizad
Department of Civil Engineering, Technical and Engineering Faculty, Mohaghegh Ardabili University, Ardabil, Iran
چکیده [English]

Isolators and dampers are devices that dissipate energy and reduce the seismic response of structures during strong earthquakes. Lead Rubber Bearings (LRBs) and Tuned Mass Dampers (TMDs) are two common types of these devices. Studying non-linear behavior and seismic properties of structural systems equipped with this type of isolators and dampers can significantly enhance understanding of their behavior against lateral forces caused by strong earthquakes. In this study, a 9-story steel structure was analyzed using the Endurance Time Analysis (ETA) method in SAP2000 software with fixed and isolated bases, along with a tuned mass damper with different mass ratios and without a tuned mass damper. The analysis results indicate that the tuned mass damper is highly effective in reducing the displacement of the isolator level, drift ratio, and story shear forces. This effect is more significant with higher mass ratios of the TMD. This effect is more pronounced with higher mass ratios of the TMD. However, reducing the absolute acceleration response of the structure has not been effective, and increasing the mass ratio of the TMD only reduces the performance of the control system in improving the absolute acceleration response compared to the isolated state.

کلیدواژه‌ها [English]

  • Lead Rubber Bearing
  • Tuned Mass Damper
  • Endurance Time Analysis
  • Steel structure
  1. M. Zafri & A. Khan, “A spatial regression modeling framework for examining relationships between the built environment and pedestrian crash occurrences at macroscopic level: a study in a developing country context,” Geography and sustainability, vol. 3, no. 4, pp.312-324, (2022).
  2. Umair, I.A. Rana & R.H Lodhi, “The impact of urban design and the built environment on road traffic crashes: a case study of Rawalpindi, Pakistan,” Case studies on transport policy, vol. 10, no. 1, pp.417-426, (2022).
  3. Chen & J. Zhou, “Effects of the built environment on automobile-involved pedestrian crash frequency and risk,” Journal of Transport & Health, vol. 3, no. 4, pp. 448-456, (2016).
  4. M. Zafri, A.A. Prithul, I. Baral & M. Rahman, “Exploring the factors influencing pedestrian-vehicle crash severity in Dhaka, Bangladesh,” International journal of injury control and safety promotion, vol. 27, no. 3, pp. 300-307, (2020).
  5. A Almasi & H.R. Behnood, “Exposure based geographic analysis mode for estimating the expected pedestrian crash frequency in urban traffic zones; case study of Tehran,” Accident Analysis & Prevention, pp. 168-179, Apr 4-11, (2022).
  6. Fuentes, R. Truffello & M. Flores, “Impact of Land Use Diversity on Daytime Social Segregation Patterns in Santiago de Chile,” Buildings, vol. 12, no. 2, pp.149, (2022).
  7. D. Kang , “The S+ 5Ds: Spatial access to pedestrian environments and walking in Seoul, Korea,” Cities, vol. 77, no. 3, pp. 130-141, (2018).
  8. Lake & L. Ferreira, “Towards a methodology to evaluate public transport projects”, 2002.
  9. H. Yoon, Y.W. Kim & Y.G. Ji, “The effects of takeover request modalities on highly automated car control transitions,” Accident Analysis & Prevention, vol. 123, no. 1, pp. 150-158, (2019).
  10. Chen, N. Sze, S. Chen, S. Labi & Q. Zeng, “Analysing the main and interaction effects of commercial vehicle mix and roadway attributes on crash rates using a Bayesian random-parameter Tobit model,” Accident Analysis & Prevention, vol. 154, no. 1, pp. 8-15,(2021).
  11. P. Tarko, “Maximum likelihood method of estimating the conflict-crash relationship,” Accident Analysis & Prevention, vol. 179, no. 1, pp. 12-19, (2023).
  12. H. Rashidi, S. Keshavarz, P. Pazari, N. Safahieh & A. Samimi, “Modeling the accuracy of traffic crash prediction models,” IATSS Research, vol. 46, no. 3, pp. 345-352, (2022).
  13. Moomen, M. Rezapour, M. Raja & K. Ksaibati, “Predicting downgrade crash frequency with the random-parameters negative binomial model: Insights into the impacts of geometric variables on downgrade crashes in Wyoming,” IATSS Research, 44(2), vol. 44, no. 2, pp. 94-102, (2020).
  14. Tang & E.T. Donnell, “Application of a model-based recursive partitioning algorithm to predict crash frequency,” Accident Analysis & Prevention, vol. 132, no. 1, pp. 105-116, (2019).
  15. Huang, Q. Zeng, X. Pei, S. Wong & P. Xu, “Predicting crash frequency using an optimised radial basis function neural network model,” Transportmetrica A: transport science, vol. 12, no. 2, pp. 330-345, (2016).
  16. Zarei, B. Hellinga & P. Izadpanah, “CGAN-EB: A non-parametric empirical Bayes method for crash frequency modeling using conditional generative adversarial networks as safety performance functions,” International Journal of Transportation Science and Technology, vol. 1, no. 1, pp. 35-46, (2022).
  17. A. Almasi, H.R. Behnood & R. Arvin, “Pedestrian crash exposure analysis using alternative geographically weighted regression models,” Journal of advanced transportation, vol. 2021, no. 1, pp. 50-63, (2021).
  18. K. Al-Aamri, G. Hornby, L.C. Zhang, A.A. Al-Maniri & S.S. Padmadas, “Mapping road traffic crash hotspots using GIS-based methods: A case study of Muscat Governorate in the Sultanate of Oman,” Spatial Statistics, 42, vol. 42, no. 1, pp. 78-85, (2021).
  19. Liu, A. Hainen, X. Li, Q. Nie & S. Nambisan, “Pedestrian injury severity in motor vehicle crashes: an integrated spatio-temporal modeling approach,” Accident Analysis & Prevention, vol. 132, no. 3, pp. 105-117, (2019).
  20. E. Iyanda, R. Adeleke, Y. Lu, T. Osayomi, A. Adaralegbe, M. Lasode & A. M. Osundina, “A retrospective cross-national examination of COVID-19 outbreak in 175 countries: a multiscale geographically weighted regression analysis,” Journal of infection and public health, vol. 13, no. 10, pp.1438-1445, (2020).
  21. Zhou, X. Jiang, C. Fu, H. Liu & G. Zhang, “Bayesian spatial correlation, heterogeneity and spillover effect modeling for speed mean and variance on urban road networks,” Accident Analysis & Prevention, vol. 174, no. 3, pp. 103-110, (2022).
  22. Galgamuwa, J. Du & S. Dissanayake, “Bayesian spatial modeling to incorporate unmeasured information at road segment levels with the INLA approach: A methodological advancement of estimating crash modification factors,” Journal of traffic and transportation engineering (English edition), vol. 8, no. 1, pp.95-106, (2021).
  23. Saha, P. Alluri, A. Gan & W. Wu, “Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models,” Accident Analysis & Prevention, vol. 118, no. 1, pp. 166-177, (2018).
  24. M. Peera, R.S. Shekhawat & C. Prasad, “Traffic analysis zone level road traffic accident prediction models based on land use characteristics,” International journal for traffic and transport engineering (Belgrade), vol. 9, no. 4, pp. 376-386, (2019).
  25. Sung, S. Lee, S. Cheon & J. Yoon, “Pedestrian Safety in Compact and Mixed-Use Urban Environments: Evaluation of 5D Measures on Pedestrian Crashes,” Sustainability, vol. 14, no. 2, pp.646, (2022).
  26. Guo & D. Lu, “How many crashes does cellphone use contribute to? Population attributable risk of cellphone use while driving,” Journal of safety research, vol. 82, no. 4, pp. 385-391, (2022).
  27. Rahmani, A. Lotfata, E. Zebardast, S. Rastegar, T.W. Sanchez, B.A. Goharrizi & S. Landi, “Land use suitability assessment for economic development at the provincial level: The case study of Yazd Province, Iran,” Sustainable Cities and Society, vol. 87, no. 1, pp. 104-112, (2022).
  28. Delclòs-Alió & C. Miralles-Guasch, “Looking at Barcelona through Jane Jacobs’s eyes: Mapping the basic conditions for urban vitality in a Mediterranean conurbation,” Land Use Policy, vol. 75, no. 1, pp. 505-517, (2018).
  29. Olsson, “Generalized linear models: an applied approach: Lund,” Student litteratur,) 2002(.
  30. R. da Silva & A.S. Fotheringham, “The multiple testing issue in geographically weighted regression,” Geographical Analysis, 48(3), vol. 48, no. 3, pp.233-247, (2016).
  31. Barmoudeh, H. Baghishani & S. Martino, “Bayesian spatial analysis of crash severity data with the INLA approach: Assessment of different identification constraints,” Accident Analysis & Prevention, vol. 167, no. 1, pp. 106-118, (2022).
  32. Mukherjee, K.R. Rao & G. Tiwari, “Built-environment risk assessment for pedestrians near bus-stops: a case study in Delhi,” International journal of injury control and safety promotion, vol. 30, no. 2, pp.185-194, (2022).
  33. Su, N. Sze & L. Bai, “A joint probability model for pedestrian crashes at macroscopic level: Roles of environment, traffic, and population characteristics,” Accident Analysis & Prevention, vol. 150, no. 1, pp. 105-113, (2021).
  34. Rojas-Rueda, M.J. Nieuwenhuijsen, H. Khreis & H. Frumkin, “Autonomous vehicles and public health,” Annual review of public health, vol. 41, no. 1, pp.329-345, (2020).
  35. Stipancic, L. Miranda-Moreno, N. Saunier & A. Labbe, “Network screening for large urban road networks: using GPS data and surrogate measures to model crash frequency and severity,” Accident Analysis & Prevention, vol. 125, no. 2, pp. 290-301, (2019).
  36. Álvarez, M. A. Fernández, A. Gordaliza, A. Mansilla & A. Molinero, “Geometric road design factors affecting the risk of urban run-off crashes,” A case-control study. PLoS one, vol. 15, no. 6, pp.50-64, (2020).
  37. Lee, M. Abdel-Aty & I. Shah, “Evaluation of surrogate measures for pedestrian trips at intersections and crash modeling,” Accident Analysis & Prevention, vol. 130, no. 1, pp. 91-98, (2019).

 

 

 

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