Estimating uplift capacity of suction caissons in soft clay: A hybrid computational approach based on model tree and GP

Stability of suction caissons used as foundations or anchors of offshore structures is a critical challenge in marine structures engineering. To this end, many studies have been conducted including those concentrate on implementing computational intelligence methods to model the response of suction caissons under loading. In this regard, this paper aims at formulating uplift capacity of suction caissons using a hybrid artificial intelligence computational tool based on model tree (M5) and genetic programming (GP), called M5-GP. The formulae are developed in terms of several governing parameters using a reliable experimental database from the literature. The results show that the M5-GP based relationships are able to predict the uplift capacity of suction caissons precisely. Furthermore, to consider the safety in the design process, probabilistic equations are also given for various risk levels. The new formulas compare favorably with the existing relationships in the literature regarding prediction performance. In addition, the simplified formulation is compact, easy to use and physically sound. Therefore, it is especially appropriate to be used in design practice.