Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling with Relief algorithm
The consolidation coefficient of soil (Cv) is a crucial parameter used for the design of structures leaned on soft soi. In general, the Cv is determined experimentally in the laboratory. However, the experimental tests are time-consuming as well as expensive. Therefore, researchers tried several ways to determine Cv via other simple soil parameters. In this study, we developed a hybrid model of Random Forest coupling with a Relief algorithm (RF-RL) to predict the Cv of soil. To conduct this study, a database of soil parameters collected from a case study region in Vietnam was used for modeling. The performance of the proposed models was assessed via statistical indicators, namely Coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The proposal models were constructed with four sets of soil variables, including 6, 7, 8, and 13 inputs. The results revealed that all models performed well with a high performance (R2 > 0.980). Although the RF-RL model with 13 variables has the highest prediction accuracy (R2 = 0.9869), the difference compared with other models was negligible (i.e., R2 = 0.9824, 0.9850, 0.9825 for the cases with 6, 7, 8 inputs, respectively). Thus, it can be concluded that the hybrid model of RF-RL can be employed to predict Cv based on the basic soil parameters.
Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling with Relief algorithm
The consolidation coefficient of soil (Cv) is a crucial parameter used for the design of structures leaned on soft soi. In general, the Cv is determined experimentally in the laboratory. However, the experimental tests are time-consuming as well as expensive. Therefore, researchers tried several ways to determine Cv via other simple soil parameters. In this study, we developed a hybrid model of Random Forest coupling with a Relief algorithm (RF-RL) to predict the Cv of soil. To conduct this study, a database of soil parameters collected from a case study region in Vietnam was used for modeling. The performance of the proposed models was assessed via statistical indicators, namely Coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The proposal models were constructed with four sets of soil variables, including 6, 7, 8, and 13 inputs. The results revealed that all models performed well with a high performance (R2 > 0.980). Although the RF-RL model with 13 variables has the highest prediction accuracy (R2 = 0.9869), the difference compared with other models was negligible (i.e., R2 = 0.9824, 0.9850, 0.9825 for the cases with 6, 7, 8 inputs, respectively). Thus, it can be concluded that the hybrid model of RF-RL can be employed to predict Cv based on the basic soil parameters.
Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling with Relief algorithm
Front. Struct. Civ. Eng.
Ly, Hai-Bang (Autor:in) / Vu, Huong-Lan Thi (Autor:in) / Ho, Lanh Si (Autor:in) / Pham, Binh Thai (Autor:in)
Frontiers of Structural and Civil Engineering ; 16 ; 224-238
01.02.2022
15 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Consolidation coefficient -- Cohesive soil mixtures
| Engineering Index Backfile | 1964
Integrating Nonlinear Dimensionality Reduction with Random Forests for Financial Distress Prediction
| British Library Online Contents | 2015
Degree of Consolidation and Reduction Coefficient concerning Differential Consolidation Settlements
| British Library Online Contents | 1998
Degree of Consolidation and Reduction Coefficient concerning Differential Consolidation Settlements
| Online Contents | 1998