Genetic-based modeling of uplift capacity of suction caissons

Authors:
Amir Hossein Alavi;Pejman Aminian;Amir Hossein Gandomi;Milad Arab Esmaeili
Affiliations:
School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran;Department of Civil Engineering, Islamic Azad University, Shahrood Branch, Shahrood, Iran;College of Civil Engineering, Tafresh University, Tafresh, Iran;Department of Civil Engineering, Islamic Azad University, Shahrood Branch, Shahrood, Iran
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 6
Cited 0

Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications

Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Prediction of uplift capacity of suction caissons using a neuro-genetic network

Engineering with Computers
Prediction of compressive and tensile strength of limestone via genetic programming

Expert Systems with Applications: An International Journal
An application of the genetic programming technique to strategy development

Expert Systems with Applications: An International Journal
Modelling damping ratio and shear modulus of sand-mica mixtures using genetic programming

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

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.