Efficient Machine Learning Model for Predicting the Stiffness of Circular Footings on Clay Overlying Sand

Abstract

Assessing the stiffness of circular foundations is the key to evaluating their deformation; thus, it is important for foundation design. The current determination methods for the stiffness coefficient are either time-consuming or inaccurate. In this paper, a novel stiffness prediction model has been proposed, using the decision tree (DT) algorithm optimized by particle size optimization (PSO). The condition of the embedded foundation, the embedded depth (ZD/2R), the thickness of the clay layer beneath the foundation base (T/2R), and the ratio of shear stiffness between clay and sand (Gsand/Gclay) were used as input variables, while the elastic stiffness coefficients (Kc, Kh, Km, and Kv) were used as output variables. The optimum DT model has undergone comprehensive validation, and independent model verification using extra simulations. The results illustrate that PSO could promote further increases in the capability of DT modeling in predicting stiffness coefficients. The optimum DT model achieved a good level of performance on stiffness coefficient modeling. (The R for the training set was greater than 0.98 for all of the stiffness coefficients.) The variable importance analysis showed that the T/2R was the most significant variable for all stiffness coefficients, followed by Gsand/Gclay. The optimum DT model achieved good predictive performance upon independent verification, with the R being 0.97, 0.99, 0.99, and 0.95 for Kv, Kh, Km, and Kc, respectively. The proposed reliable and efficient DT-PSO model for stiffness coefficients in layered soil could further promote the safe and efficient utilization of circular foundations.