Machine learning-based prediction of ‎seismic response of structures

Kierownik projektu: Neda Asgarkhani

Politechnika Gdańska

Wydział Inżynierii Lądowej i Środowiska

Gdańsk

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".‎




← Powrót do spisu projektów

KONTAKT

Nasi konsultanci służą pomocą przyszłym i początkującym użytkownikom specjalistycznego oprogramowania zainstalowanego na Komputerach Dużej Mocy w Centrum Informatycznym TASK.

Kontakt w sprawach Komputerów Dużej Mocy, oprogramowania/licencji, grantów obliczeniowych, sprawozdań:

kdm@task.gda.pl

Administratorzy odpowiadają na maile w dni robocze w godzinach 8:00 – 15:00.