Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Ensemble Tree-Based Approach to Predict the Rotation Capacity of Wide-Flange Beams
The rotation capacity of wide-flange beams is a mechanical and physical parameter that shows a structural member’s ductility. It is a crucial factor in the plastic design phase of wide-flange beams, especially useful in extreme circumstances such as earthquakes. This study proposes an approach that facilitates the calculation of the rotation capacity (R) based on a soft computing technique developed using an experimental database accumulated from prior studies. The ensemble decision tree (EDT) model was studied to construct a soft computing model that accurately predicts R based on training and testing datasets. The model’s performance metrics used were well-known criteria, namely the coefficient of determination (CC), root mean square error (RMSE), as well as mean absolute error (MAE). With CC = 0.925, RMSE of 3.20, and MAE of 2.60, the study’s findings indicate that the EDT model accurately estimates the rotation capacity of wide-flange steel beams. Furthermore, sensitivity analysis and 2D partial dependence analyses were proposed to determine the effect of the factors that affect R. This work could be a significant step toward determining the R of wide-flange steel beams and aiding in improving structural member design.
Ensemble Tree-Based Approach to Predict the Rotation Capacity of Wide-Flange Beams
The rotation capacity of wide-flange beams is a mechanical and physical parameter that shows a structural member’s ductility. It is a crucial factor in the plastic design phase of wide-flange beams, especially useful in extreme circumstances such as earthquakes. This study proposes an approach that facilitates the calculation of the rotation capacity (R) based on a soft computing technique developed using an experimental database accumulated from prior studies. The ensemble decision tree (EDT) model was studied to construct a soft computing model that accurately predicts R based on training and testing datasets. The model’s performance metrics used were well-known criteria, namely the coefficient of determination (CC), root mean square error (RMSE), as well as mean absolute error (MAE). With CC = 0.925, RMSE of 3.20, and MAE of 2.60, the study’s findings indicate that the EDT model accurately estimates the rotation capacity of wide-flange steel beams. Furthermore, sensitivity analysis and 2D partial dependence analyses were proposed to determine the effect of the factors that affect R. This work could be a significant step toward determining the R of wide-flange steel beams and aiding in improving structural member design.
Ensemble Tree-Based Approach to Predict the Rotation Capacity of Wide-Flange Beams
Thuy-Anh Nguyen (Autor:in) / Hai-Bang Ly (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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