Formulation of uplift capacity of suction caissons using multi expression programming

Abstract

Suction caissons have increasingly been used as foundations and anchors for deepwater offshore structures in the last decade. The increased use of suction caissons defines a serious need to develop more authentic methods for simulating their behavior. Reliable assessment of uplift capacity of caissons in cohesive soils is a critical issue facing design engineers. This paper proposes a new approach for the formulation of the uplift capacity of suction caissons using a promising variant of Genetic Programming (GP), namely Multi Expression Programming (MEP). The proposed model is developed based on experimental results obtained from the literature. The derived MEP-based formula takes into account the effect of aspect ratio of caisson, shear strength of clayey soil, point of application and angle of inclination of loading, soil permeability and loading rate. A subsequent parametric analysis is carried out and the trends of the results are confirmed via previous studies. The results indicate that the MEP formulation can predict the uplift capacity of suction caissons with an acceptable level of accuracy. The proposed formula provides a prediction performance better than or comparable with the models found in the literature. The MEP-based simplified formulation is particularly valuable for providing an analysis tool accessible to practicing engineers.

Development of prediction models for shear strength of SFRCB using a machine learning approach

“…Different parameters were taken for the algorithm to explore the optimal model. This was based on the values recommended by other researchers [24,28,29], as well as a number of preliminary runs. As can be seen in Table 3, 3 values were checked for the population size and number of generation.…”

“…Oltean and Grosan [27] showed that the advantage of MEP over other modeling techniques. There are few applications of this powerful tool in civil engineering domain [28,29]. It is worth mentioning that MEP possesses obvious advantage over traditional statistical modeling techniques because it does not need a preliminary definition of the form of the existing relationships.…”

Multi‐expression programming based model for prediction of formation enthalpies of nitro‐energetic materials

“…Using the numerical experiments, the MEP method has the capability to overtake similar techniques (Oltean & Grossan, ). Despite this algorithm having the prediction capability, MEP is not widely applied in engineering (Alavi et al ., ; Alavi & Gandomi, 2012; Gandomi et al ., ).…”

Prediction of Uplift Capacity of Suction Caisson in Clay Using Functional Network and Multivariate Adaptive Regression Spline

“…Alavi et al [39] found that a hybrid computational model (coupling of GP and simulated annealing) provided a better prediction performance than GP did, by predicting the uplift capacity of suction caisson using the above database (Rahman et al [26]). Gandomi et al [40] proposed a promising variant of GP, namely Multi-Expression Programming (MEP), and proved its higher capability in prediction performance compared to ANN and FEM models. Multivariate Adaptive Regression Spline (MARS), developed by Friedman [41], is a exible regression model that ts relationships which are nearly additive and involve lesser variables.…”