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Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
https://orcid.org/0000-0002-6741-115X
Abstract 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 search, fine-tuning method, and the k-fold cross-validation, to derive the seismic fragility curve for accelerating seismic risk assessment. Proposed ML methods significantly reduced the computational efforts compared to conventional procedure of seismic fragility assessment. The prediction results can be combined with considered hazard curves for the purpose of seismic risk assessment of RC buildings. To prepare the training dataset, Incremental Dynamic Analyses (IDAs) were performed on 165 RC frames to achieve 1121184 data points. Performance indicators showed that the algorithms of Artificial Neural Networks (ANNs), Extra-Trees Regressor (ETR), Extremely Randomized Tree Regressor (ERTR), Bagging Regressor (BR), Extreme Gradient Boosting (XGBoost), and Histogram-based Gradient Boosting Regression (HGBR) had higher performance, which achieved acceptable accuracy and fitted to actual curves. In addition, Graphical User Interface (GUI) was introduced as a practical tool yet reliable for seismic risk assessment of RC buildings.
Highlights Machine Learning (ML) algorithms were proposed to predict seismic fragility curve of Reinforced Concrete (RC) buildings. ML algorithms were trained to build prediction models for seismic vulnerability and risk assessment of RC buildings. TThe indicators showed that proposed prediction models had higher performance while delicately fitted to actual curves. Graphical User Interface was developed to prepare prediction tool for seismic risk assessment and plot probabilistic curves.
Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
https://orcid.org/0000-0002-6741-115X
Abstract 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 search, fine-tuning method, and the k-fold cross-validation, to derive the seismic fragility curve for accelerating seismic risk assessment. Proposed ML methods significantly reduced the computational efforts compared to conventional procedure of seismic fragility assessment. The prediction results can be combined with considered hazard curves for the purpose of seismic risk assessment of RC buildings. To prepare the training dataset, Incremental Dynamic Analyses (IDAs) were performed on 165 RC frames to achieve 1121184 data points. Performance indicators showed that the algorithms of Artificial Neural Networks (ANNs), Extra-Trees Regressor (ETR), Extremely Randomized Tree Regressor (ERTR), Bagging Regressor (BR), Extreme Gradient Boosting (XGBoost), and Histogram-based Gradient Boosting Regression (HGBR) had higher performance, which achieved acceptable accuracy and fitted to actual curves. In addition, Graphical User Interface (GUI) was introduced as a practical tool yet reliable for seismic risk assessment of RC buildings.
Highlights Machine Learning (ML) algorithms were proposed to predict seismic fragility curve of Reinforced Concrete (RC) buildings. ML algorithms were trained to build prediction models for seismic vulnerability and risk assessment of RC buildings. TThe indicators showed that proposed prediction models had higher performance while delicately fitted to actual curves. Graphical User Interface was developed to prepare prediction tool for seismic risk assessment and plot probabilistic curves.
Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
https://orcid.org/0000-0002-6741-115X
Abstract 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 search, fine-tuning method, and the k-fold cross-validation, to derive the seismic fragility curve for accelerating seismic risk assessment. Proposed ML methods significantly reduced the computational efforts compared to conventional procedure of seismic fragility assessment. The prediction results can be combined with considered hazard curves for the purpose of seismic risk assessment of RC buildings. To prepare the training dataset, Incremental Dynamic Analyses (IDAs) were performed on 165 RC frames to achieve 1121184 data points. Performance indicators showed that the algorithms of Artificial Neural Networks (ANNs), Extra-Trees Regressor (ETR), Extremely Randomized Tree Regressor (ERTR), Bagging Regressor (BR), Extreme Gradient Boosting (XGBoost), and Histogram-based Gradient Boosting Regression (HGBR) had higher performance, which achieved acceptable accuracy and fitted to actual curves. In addition, Graphical User Interface (GUI) was introduced as a practical tool yet reliable for seismic risk assessment of RC buildings.
Highlights Machine Learning (ML) algorithms were proposed to predict seismic fragility curve of Reinforced Concrete (RC) buildings. ML algorithms were trained to build prediction models for seismic vulnerability and risk assessment of RC buildings. TThe indicators showed that proposed prediction models had higher performance while delicately fitted to actual curves. Graphical User Interface was developed to prepare prediction tool for seismic risk assessment and plot probabilistic curves.
Machine learning-based seismic fragility and seismic vulnerability assessment of reinforced concrete structures
Kazemi, F. (author) / Asgarkhani, N. (author) / Jankowski, R. (author)
2023-01-02
Article (Journal)
Electronic Resource
English
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