Piles are used in substructures of different infrastructural constructions. Due to the complex nature of soil, there are different empirical models to predict the bearing capacity of piles. The objective of the presen...
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Piles are used in substructures of different infrastructural constructions. Due to the complex nature of soil, there are different empirical models to predict the bearing capacity of piles. The objective of the present study is to develop prediction models for vertical loaded driven piles in cohesionless soil using a novel artificial intelligence (AI) technique multi-objective genetic programming (MOGP). Two other recent AI techniques, multivariate adaptive regression spline (MARS) and functional network (FN), are also used to compare the efficacy of different AI techniques. The results MOGP, MARS and FN models are compared in terms of different statistical parameters such as correlation coefficient (R), absolute average error, root-mean-square-error, overfitting ratio and P-50. A ranking criteria approach has been implemented to assess the performance of the prediction models developed in this study along with other AI and empirical models available in the literature. The predictive model equations based on MOGP, MARS and FN are also presented.
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