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Neda Asgarkhani
Employment
- Ph.D. student at Gdasnk university of technology
Keywords Help
- data-driven techniques
- 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
- computational method, active learning, ensemble machine-learning mod-el, retrofitting structures, mainshock-aftershock sequence.
- computational optimization
- cross-sectional area
- dome structures.
- double-stage yield buckling-restrained brace steel slit damper experimental validation cyclic loading test novel bracing system seismic retrofit energy dissipation devices
- fiber-reinforced polymer
- high-performance alkali-activated concrete compressive strength cost and carbon emission machine learning algorithms steel fiber
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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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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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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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