Investigating pounding between structures during earthquakes
Identyfikator grantu: PT00922
Kierownik projektu: 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).
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
- 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
- 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
- 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
- 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
- Kazemi, F., & Jankowski, R, Seismic performance evaluation of steel buckling-restrained braced frames including SMA materials, Journal of Constructional Steel Research 107750, (2023) 201
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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