Genetic-based modeling of uplift capacity of suction caissons

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.

► Classical tree-based genetic programming (TGP), linear genetic programming (LGP) and gene expression programming (GEP) are utilized to develop new prediction models for uplift capacity of suction caissons.
► The LGP model has better overall behavior followed by the GEP and TGP models.
► The results of TGP, LGP and GEP are better than or comparable with those obtained by regression, finite element method (FEM), artificial neural network (ANN) and hybrid neuro-genetic (NGN).
► Using any form of optimally controlling the parameters can improve the performance of the TGP, LGP and GEP algorithms.