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Efficient hybrid machine learning model for calculating load-bearing capacity of driven piles
https://orcid.org/http://orcid.org/0000-0001-6537-7835
https://orcid.org/http://orcid.org/0000-0001-7696-3450
The aim of this study is to develop an efficient hybrid machine learning (ML) model, which combines the genetic algorithm (GA) and artificial neural network (ANN) for rapidly calculating the load-bearing capacity (LBC) reinforced concrete driven piles. An extensive database including 470 static tests is collected to train the hybrid ML model. The predicted results of the GA–ANN model in this study are compared to those of the pure ANN model. Statistical indicators containing the coefficient of determination (R2), root-mean-squared error (RMSE), and a20-index are determined to assess the prediction performance of the ML models. The comparison emphasizes that the GA–ANN model predicts the LBC of the pile accurately with a very high R2 value of 0.99 and small RMSE of 49 kN. Furthermore, the effects of input variables on the predicted LBC are evaluated. Finally, to apply the ML model, a graphical user interface tool is developed for simplifying the LBC of reinforced concrete driven piles.
Efficient hybrid machine learning model for calculating load-bearing capacity of driven piles
https://orcid.org/http://orcid.org/0000-0001-6537-7835
https://orcid.org/http://orcid.org/0000-0001-7696-3450
The aim of this study is to develop an efficient hybrid machine learning (ML) model, which combines the genetic algorithm (GA) and artificial neural network (ANN) for rapidly calculating the load-bearing capacity (LBC) reinforced concrete driven piles. An extensive database including 470 static tests is collected to train the hybrid ML model. The predicted results of the GA–ANN model in this study are compared to those of the pure ANN model. Statistical indicators containing the coefficient of determination (R2), root-mean-squared error (RMSE), and a20-index are determined to assess the prediction performance of the ML models. The comparison emphasizes that the GA–ANN model predicts the LBC of the pile accurately with a very high R2 value of 0.99 and small RMSE of 49 kN. Furthermore, the effects of input variables on the predicted LBC are evaluated. Finally, to apply the ML model, a graphical user interface tool is developed for simplifying the LBC of reinforced concrete driven piles.
Efficient hybrid machine learning model for calculating load-bearing capacity of driven piles
https://orcid.org/http://orcid.org/0000-0001-6537-7835
https://orcid.org/http://orcid.org/0000-0001-7696-3450
The aim of this study is to develop an efficient hybrid machine learning (ML) model, which combines the genetic algorithm (GA) and artificial neural network (ANN) for rapidly calculating the load-bearing capacity (LBC) reinforced concrete driven piles. An extensive database including 470 static tests is collected to train the hybrid ML model. The predicted results of the GA–ANN model in this study are compared to those of the pure ANN model. Statistical indicators containing the coefficient of determination (R2), root-mean-squared error (RMSE), and a20-index are determined to assess the prediction performance of the ML models. The comparison emphasizes that the GA–ANN model predicts the LBC of the pile accurately with a very high R2 value of 0.99 and small RMSE of 49 kN. Furthermore, the effects of input variables on the predicted LBC are evaluated. Finally, to apply the ML model, a graphical user interface tool is developed for simplifying the LBC of reinforced concrete driven piles.
Efficient hybrid machine learning model for calculating load-bearing capacity of driven piles
Asian J Civ Eng
Nguyen, Trong-Ha (author) / Nguyen, Kieu-Vinh Thi (author) / Ho, Viet-Chuong (author) / Nguyen, Duy-Duan (author)
Asian Journal of Civil Engineering ; 25 ; 883-893
2024-01-01
11 pages
Article (Journal)
Electronic Resource
English
Efficient hybrid machine learning model for calculating load-bearing capacity of driven piles
| Springer Verlag | 2024
Calculating bearing capacity of piles
| Engineering Index Backfile | 1965
Load-bearing capacity of piles
| Engineering Index Backfile | 1963
Bearing capacity of driven piles
| Engineering Index Backfile | 1930