Bearing Capacity of Shallow Foundations on Cohesionless Soils: A Random Forest Based Approach

Abstract

Determining the ultimate bearing capacity (UBC) is vital for design of shallow foundations. Recently, soft computing methods (i.e. artificial neural networks and support vector machines) have been used for this purpose. In this paper, Random Forest (RF) is utilized as a tree-based ensemble classifier for predicting the UBC of shallow foundations on cohesionless soils. The inputs of model are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ) and internal friction angle (ϕ). A set of 112 load tests data were used to calibrate and test the developed RF-based model. The used data set consists of 47 full-scale observations and 65 small-scale laboratory footing load tests. To demonstrate the efficiency of proposed RF-based model, the results are compared with some popular classical formulas that are most commonly used for determining the UBC. The results show the efficiency and capabilities of the proposed RF-based model as a practical tool in evaluating the UBC of shallow foundations in a fast and accurate way.

Keywords

• artificial intelligence
• decision tree
• random forest (rf)
• shallow foundations
• ultimate bearing capacity