Application of bagging ensemble model for predicting compressive strength of hollow concrete masonry prism
In the current research, a newly developed ensemble intelligent predictive model called Bagging Regression (BGR) is proposed to predict the compressive strength of a hollow concrete masonry prism (fp). A matrix of input combinations is constructed based on several predictive variables, including mor...
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/64843f78f4834b028aad1ee593b10856 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | In the current research, a newly developed ensemble intelligent predictive model called Bagging Regression (BGR) is proposed to predict the compressive strength of a hollow concrete masonry prism (fp). A matrix of input combinations is constructed based on several predictive variables, including mortar compressive strength (fm), concrete block compressive strength (fb), and height to thickness ratio (h/t). Three modeling scenarios based on the different data divisions (i.e., 80–20%, 75–25%, and 70–30%) for training-testing phases are evaluated. The proposed model is validated against classical support vector regression (SVR) and decision tree regression (DTR) models using statistical indicators and graphical presentations. Results indicate the superiority of the BGR over the other models. In quantitative terms, BGR attains minimum root mean square error (RMSE = 1.51 MPa) using the data division scenario of 80–20% in the testing phase, while DTR and standalone SVR models offer RMSE = 2.55 and 2.33 MPa, respectively. |
|---|