Zastosowania

Investigating pounding between structures during earthquakes

Identyfikator grantu: PT00922

Kierownik projektu: Farzin Kazemi

Realizatorzy:

  • Farzin Kazemi

Politechnika Gdańska

Wydział Inżynierii Lądowej i Środowiska

Gdańsk

Data otwarcia: 2021-12-16

Planowana data zakończenia grantu: 2025-12-16

Streszczenie projektu

In the study, a comprehensive study on the effects of pounding between ‎structures during earthquakes was performed. The novelty of the study was to ‎perform Incremental Dynamic Analyses (IDAs) to compute the seismic collapse ‎capacities of both pounding structures in one model and automatically removal ‎of the collapsed structure during analyses, which can be used to reduce ‎analytical efforts. Moreover, modification factors were proposed to ‎approximately estimate the median collapse capacity of single structures to ‎consider the effect of pounding, which cannot be considered before design ‎process. In addition, the modification factors can be used for single structures ‎that were retrofitted with linear and nonlinear Fluid Viscous Dampers (FVDs). ‎To compute the seismic collapse capacities of two or three pounding ‎structures, an algorithm to automated removal the ‎collapsed structure during ‎IDAs using Matlab and OpenSees ‎softwares was developed.‎ Using modification ‎factors, it is possible to predict the seismic collapse capacity of a structure in ‎pounding condition and when it retrofitted with linear or nonlinear FVDs, ‎without involving in complicated modeling and analytical difficulties. Some ‎papers regarding this modeling was published.‎ In the project of doctoral thesis, we have many models of pounding structures ‎which should be performed analysis to have results. To do this, an algorithm to ‎use Matlab and Opensees software simultaneously was developed. Each model ‎should run with a system and it takes between 3 to 5 days to have results, ‎depending to the kind of systems. Therefore, at least 8 systems which can help ‎to perform analysis faster is needed. Software needed: Matlab 2014 or newer ‎version, TCL editor, Notepad++ and Opensees 2.5.0 version, which are open ‎access and can be install in any systems. The virtual machines (CI TASK cloud ‎computing) can be 4 corei systems with selected information in site (4 VCPU 10 ‎GB RAM 40 GB SSD).‎

Publikacje

  1. Kazemi, F., & Jankowski, R‎, Enhancing seismic performance of rigid and semi-rigid ‎connections equipped with SMA bolts incorporating ‎nonlinear soil-structure interaction‎, Engineering Structures‎ ‎‎114896‎, (2023) 274‎
  2. Kazemi, F., Asgarkhani, N., & Jankowski, R‎, Probabilistic assessment of SMRFs with infill masonry ‎walls incorporating nonlinear soil-structure interaction‎, Bulletin of Earthquake Engineering‎ ‎‎-, (2023) 1-32‎
  3. Kazemi, F., Asgarkhani, N., Manguri, A., & Jankowski, R‎, Investigating an optimal computational strategy to retrofit ‎buildings with implementing viscous dampers‎, International Conference on Computational Science ‎ -, (2022) ‎184-191‎
  4. Asgarkhani, N., Kazemi, F., & Jankowski, R‎, Optimal retrofit strategy using viscous dampers between ‎adjacent RC and SMRFs prone to earthquake-induced ‎pounding‎, Archives of Civil and Mechanical Engineering‎ 23, (2023) 1-26
  5. Kazemi, F., & Jankowski, R‎, Seismic performance evaluation of steel buckling-restrained ‎braced frames including SMA materials‎, Journal of Constructional Steel Research‎ ‎107750‎, (2023) 201
  6. Kazemi, F., & Jankowski, R‎, Machine learning-based prediction of seismic limit-state ‎capacity of steel moment-resisting frames considering soil-‎structure interaction‎, Computers & Structures‎ ‎106886‎, (2023) 274
  7. Kazemi, F., Asgarkhani, N., & Jankowski, R‎, Predicting seismic response of SMRFs founded on different ‎soil types using machine learning techniques‎, Engineering Structures‎ ‎114953‎, (2023) 274
  8. Kazemi, F., Asgarkhani, N., & Jankowski, R‎, Machine learning-based seismic fragility and seismic ‎vulnerability assessment of reinforced concrete structures‎, Soil Dynamics and Earthquake Engineering‎ ‎107761‎, (2023) 166
  9. Mohebi B., Kazemi F., Asgarkhani N., Ghasemnezhadsani ‎P., & Mohebi A. ‎, Performance of Vector-valued Intensity Measures for ‎Estimating Residual Drift of Steel MRFs with Viscous ‎Dampers‎, International Journal of Structural and Civil Engineering ‎Research‎ 4, (2022) ‎79-83‎
  10. Dehestani, A., Kazemi, F., Abdi, R., & Nitka, M‎., Prediction of fracture toughness in fibre-reinforced concrete, ‎mortar, and rocks using various machine learning ‎techniques.‎, Engineering Fracture Mechanics‎ ‎108914‎, (2022) 276
  11. Manguri, A., Saeed, N., Kazemi, F., Szczepanski, M., & ‎Jankowski, R. ‎, Optimum number of actuators to minimize the cross-‎sectional area of prestressable cable and truss structures.‎, Structures ‎ 47, (2023) ‎2501-2514‎
  12. Farzin Kazemi, Torkan Shafighfard, Doo-Yeol Yoo, Data-Driven Modeling of Mechanical Properties of Fiber-Reinforced Concrete: A Critical Review, Archives of Computational Methods in Engineering 1, (2024) 1-30
  13. Farzam Omidi Moaf, Farzin Kazemi, Hakim S Abdelgader, Marzena Kurpińska, Machine learning-based prediction of preplaced aggregate concrete characteristics, Engineering Applications of Artificial Intelligence 2, (2023) 94
  14. Benyamin Mohebi, Farzin Kazemi, Atefeh Yousefi, Enhancing Seismic Performance of Semi-rigid Connection Using Shape Memory Alloy Bolts Considering Nonlinear Soil–Structure Interaction, Eurasian Conference on OpenSees 3, (2023) 248-256
  15. Benyamin Mohebi, Farzin Kazemi, Atefeh Yousefi, Seismic Response Analysis of Knee-Braced Steel Frames Using Ni-Ti Shape Memory Alloys (SMAs), Eurasian Conference on OpenSees 4, (2023) 238-247
  16. Benyamin Mohebi, Mohammad Sartipi, Farzin Kazemi, Enhancing seismic performance of buckling-restrained brace frames equipped with innovative bracing systems, Archives of Civil and Mechanical Engineering 5, (2023) 243
  17. Farzin Kazemi, Neda Asgarkhani, Robert Jankowski, Machine learning-based seismic response and performance assessment of reinforced concrete buildings, Archives of Civil and Mechanical Engineering 6, (2023) 94
  18. Neda Asgarkhani, Farzin Kazemi, Robert Jankowski, Machine learning-based prediction of residual drift and seismic risk assessment of steel moment-resisting frames considering soil-structure interaction, Computers & Structures 289, (2023) 107181
  19. Farzin Kazemi, Neda Asgarkhani, Ahmed Manguri, Natalia Lasowicz, Robert Jankowski, Introducing a computational method to retrofit damaged buildings under seismic mainshock-aftershock sequence, International Conference on Computational Science 6, (2023) 180-187
  20. Neda Asgarkhani, Farzin Kazemi, Anna Jakubczyk-Gałczyńska, Benyamin Mohebi, Robert Jankowski, Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods, Engineering Applications of Artificial Intelligence 7, (2024) 107388
  21. Farzin Kazemi, Neda Asgarkhani, Ahmed Manguri, Robert Jankowski, Seismic probabilistic assessment of steel and reinforced concrete structures including earthquake-induced pounding, Archives of Civil and Mechanical Engineering 24, (2024) 124
  22. Neda Asgarkhani, Farzin Kazemi, Robert Jankowski, Active learning on ensemble machine-learning model to retrofit buildings under seismic mainshock-aftershock sequence, International Conference on Computational Science 1, (2024) 470-478
  23. Farzin Kazemi, Neda Asgarkhani, Torkan Shafighfard, Robert Jankowski, Doo-Yeol Yoo, Machine-Learning Methods for Estimating Performance of Structural Concrete Members Reinforced with Fiber-Reinforced Polymers, Archives of Computational Methods in Engineering 31, (2024) 1-33
  24. Farzin Kazemi, Neda Asgarkhani, Natalia Lasowicz, Robert Jankowski, Development and experimental validation of a novel double-stage yield steel slit damper-buckling restrained brace, Engineering Structures 315, (2024) 1
  25. Torkan Shafighfard, Farzin Kazemi, Neda Asgarkhani, Doo-Yeol Yoo, Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete, Engineering Applications of Artificial Intelligence 136, (2024) 1
  26. Farzin Kazemi, Neda Asgarkhani, Robert Jankowski, Optimization-based stacked machine-learning method for seismic probability and risk assessment of reinforced concrete shear walls, Expert Systems with Applications 255, (2024) 1
  27. Farzin Kazemi, Torkan Shafighfard, Robert Jankowski, Doo-Yeol Yoo, Active learning on stacked machine learning techniques for predicting compressive strength of alkali-activated ultra-high-performance concrete, Archives of Civil and Mechanical Engineering 25, (2025) 24

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