Machine learning-based prediction of ‎seismic response of structures

Identyfikator grantu: PT00998

Kierownik projektu: Neda Asgarkhani

Politechnika Gdańska

Wydział Inżynierii Lądowej i Środowiska


Data otwarcia: 2022-10-07

Streszczenie projektu

The vulnerability of a building can be evaluated either by the in-situ technique of ‎data analysis with non-constructive methods, known as structural health ‎monitoring, or numerical analysis of structural models. The main idea of using ‎such methods is to evaluate the performance of a building in its operating ‎condition. Although the in-situ technique can provide a wide range of data, some ‎practical limitations such as implementing the sensors and mechanical ‎problems during the time can prevent the performance assessment of a ‎building. Therefore, this method can be improved by response prediction ‎methods for buildings subjected to seismic excitations.‎
Nowadays, the seismic probabilistic assessment of a building needs to perform ‎complicated analysis using a precise finite element model, which is hard for the civil ‎engineering community. Due to the unpredictable nature of ground motions, it is ‎necessary to estimate the nonlinear structural response during seismic loads to ‎take precautions for reducing the probability of collapse risk. The most ‎common ways of estimating seismic response are conducting nonlinear time ‎history analysis and Incremental Dynamic Analysis (IDA) using prior seismic ‎events and finite element methods. The prediction of seismic response using ‎these approaches needed to model complex models and perform time-consuming analysis, while using simplified models (e.g. single-degree of ‎freedom model) are computationally efficient with low performance and ‎behaviour compared to the real structures. Therefore, there is a need to ‎introduce a novel ML-based method to efficiently and accurately predict the ‎seismic response of structures. Predicting the seismic limit-state capacities of ‎structures can help engineers to find a preliminary estimation for the ‎performance levels of the designed structure.‎
This research aims to implement the most well-known Machine Learning ‎‎(ML) ‎algorithms in Python software to propose a solution for predicting ‎seismic ‎responses. To prepare the training and testing datasets for developing ‎data-driven decision techniques, IDAs were ‎performed considering Reinforced ‎Concrete Moment-Resisting Frames (RC ‎MRFs) and steel MRFs with different ‎story levels and structural plans assuming ‎seismic excitations introduced by ‎seismic provisions. Then, whole models ‎should be models in Opensees and ‎analysis should be done based on this ‎software, which may take some days for ‎each model. Then, important ‎structural features were considered in datasets to ‎train and test the ML-based ‎prediction models to find the most precise ‎algorithms for seismic probabilistic ‎prediction of steel and RC MRFs. We ‎submitted three papers regarding this ‎method.‎
In the doctoral thesis, we have many models of structures, in which ‎seismic ‎analysis should be performed using cloud systems. 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 on the kind of system. Therefore, we need at ‎least 8 systems which ‎can help to perform analysis. Software needed: ‎Windows 64-bit, Matlab 2020 ‎or newer version, TCL editor, Notepad++, ‎Python and Opensees 2.5.0, which ‎are open access and can be installed in any ‎system. The virtual machines (CI ‎TASK cloud computing) can be 8 ‎systems with selected information on the ‎website (4 VCPU 10 GB RAM 40 GB SSD). ‎We also need a system with the ‎information of "8 VCPU 20 GB RAM 80 GB ‎SSD".‎


  1. Farzin Kazemi, Neda Asgarkhani, Robert Jankowski, Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures, Soil Dynamics and Earthquake Engineering 166, (2023) 1
  2. Farzin Kazemi, Neda Asgarkhani, Robert Jankowski, Predicting seismic response of SMRFs founded on different soil types using machine learning techniques, Engineering Structures 274, (2023) 1
  3. Farzin Kazemi, Neda Asgarkhani, Robert Jankowski, Machine learning-based seismic response and performance assessment of reinforced concrete buildings, Archives of Civil and Mechanical Engineering 23, (2023) 1
  4. Farzin Kazemi, Neda Asgarkhani, Robert Jankowski, Probabilistic assessment of SMRFs with infill masonry walls incorporating nonlinear soil-structure interaction, Bulletin of Earthquake Engineering 21, (2023) 1
  5. 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) 1
  6. 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 1, (2023) 180-187
  8. Farzin Kazemi, Benyamin Mohebi, Neda Asgarkhani, Atefeh Yousefi, Advanced Scalar-valued Intensity Measures for Residual Drift Prediction of SMRFs with Fluid Viscous Dampers, International Journal of Structural Integrity 12, (2023) 20-25

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