Name
Abstract | ||
|---|---|---|
In this study, classical tree-based genetic programming (TGP) and its recent variants, namely linear genetic programming (LGP) and gene expression programming (GEP) are utilized to develop new prediction equations for the uplift capacity of suction caissons. The uplift capacity is formulated in terms of several inflecting variables. An experimental database obtained from the literature is employed to develop the models. Further, a conventional statistical analysis is performed to benchmark the proposed models. Sensitivity and parametric analyses are conducted to verify the results. TGP, LGP and GEP are found to be effective methods for evaluating the horizontal, vertical and inclined uplift capacity of suction caissons. The TGP, LGP and GEP models reach a prediction performance better than or comparable with the models found in the literature. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.eswa.2011.04.049 | Expert Syst. Appl. |
Keywords | Field | DocType |
linear genetic programming,uplift capacity,gep model,prediction performance,inclined uplift capacity,standard genetic programming,suction caisson,formulation,suction caissons,genetic-based modeling,gene expression programming,conventional statistical analysis,classical tree-based genetic programming,new prediction equation,statistical analysis | Data mining,Gene expression programming,Computer science,Genetic programming,Parametric statistics,Caisson,Suction,Linear genetic programming,Statistical analysis | Journal |
Volume | Issue | ISSN |
38 | 10 | Expert Systems With Applications |
Citations | PageRank | References |
7 | 0.79 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amir Hossein Alavi | 1 | 1016 | 45.59 |
| Pejman Aminian | 2 | 27 | 2.46 |
| Amir Hossein Gandomi | 3 | 1836 | 110.25 |
| Milad Arab Esmaeili | 4 | 19 | 2.22 |