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Neda Asgarkhani
Employment
- Ph.D. student at Gdasnk university of technology
Keywords Help
- buckling-restrained braced frame machine-learning algorithm residual interstory drift seismic retrofit seismic performance curve seismic failure probability
- computational method - damaged-building - retrofitting of buildings - mainshock-aftershock sequence
- data-driven techniques
- infill masonry wall · nonlinear soil-structure interaction · seismic limit-state capacity · seismic collapse probability · seismic retrofit · damage identification
- machine learning algorithm
- machine learning algorithm soil-structure interaction seismic risk assessment residual interstory drift seismic demand seismic failure probability
- machine learning method · maximum interstory drift ratio · seismic limit-state capacity · predicting seismic performance · seismic probabilistic assessment
- maximum interstory drift ratio
- scalar-valued intensity measure, residual drift, spectral shape, fluid viscous damper, incremental dynamic analysis
- seismic risk assessment machine learning method seismic fragility curve prediction seismic limit-state capacity seismic vulnerability assessment reinforced concrete
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Publication showcase
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Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
Many studies have been performed to put quantifying uncertainties into the seismic risk assessment of reinforced concrete (RC) buildings. This paper provides a risk-assessment support tool for purpose of retrofitting and potential design strategies of RC buildings. Machine Learning (ML) algorithms were developed in Python software by innovative methods of hyperparameter optimization, such as halving search, grid search, random...
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Predicting seismic response of SMRFs founded on different soil types using machine learning techniques
Predicting the Maximum Interstory Drift Ratio (M-IDR) of Steel Moment-Resisting Frames (SMRFs) is a useful tool for designers to approximately evaluate the vulnerability of SMRFs. This study aims to explore supervised Machine Learning (ML) algorithms to build a surrogate prediction model for SMRFs to reduce the need for complex modeling. For this purpose, twenty well-known ML algorithms implemented in Python software are trained...
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Machine learning-based seismic response and performance assessment of reinforced concrete buildings
Complexity and unpredictability nature of earthquakes makes them unique external loads that there is no unique formula used for the prediction of seismic responses. Hence, this research aims to implement the most well-known Machine Learning (ML) methods in Python software to propose a prediction model for seismic response and performance assessment of Reinforced Concrete Moment-Resisting Frames (RC MRFs). To prepare 92,400 data...
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