The accurate prediction of the bearing capacity of ring footings, which is crucial for civil engineering projects, has historically posed significant challenges. Previous research in this area has been constrained by considering only a limited number of parameters or utilizing relatively small datasets. To overcome these limitations, a comprehensive finite element limit analysis (FELA) was conducted to predict the bearing capacity of ring footings. The study considered a range of effective parameters, including clay undrained shear strength, heterogeneity factor of clay, soil friction angle of the sand layer, radius ratio of the ring footing, sand layer thickness, and the interface between the ring footing and the soil. An extensive dataset comprising 80,000 samples was assembled, exceeding the limitations of previous research. The availability of this dataset enabled more robust and statistically significant analyses and predictions of ring footing bearing capacity. In light of the time-intensive nature of gathering a substantial dataset, a customized deep neural network (DNN) was developed specifically to predict the bearing capacity of the dataset rapidly. Both computational and comparative results indicate that the proposed DNN (i.e. DNN-4) can accurately predict the bearing capacity of a soil with an R2 value greater than 0.99 and a mean squared error (MSE) below 0.009 in a fraction of 1 s, reflecting the effectiveness and efficiency of the proposed method.

Download PDF:

Keywords: Bearing capacity, Ring footing, Finite element limit analysis (FELA), BC-RF dataset, Deep neural network (DNN)