Rock Mechanics
Jitendra Singh Yadav; Anant Saini
Abstract
The goal of this research work was to use an Artificial Neural Network (ANN) model to predict the ultimate bearing capacity of circular footing resting on recycled construction waste over loose sand. A series of plate load tests were conducted by varying the thickness of two sizes of recycled construction ...
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The goal of this research work was to use an Artificial Neural Network (ANN) model to predict the ultimate bearing capacity of circular footing resting on recycled construction waste over loose sand. A series of plate load tests were conducted by varying the thickness of two sizes of recycled construction waste (5 mm and 10.6 mm) layer (0.4d, 0.6d, 0.8d, 1d, and 1.2d, d: diameter of footing) prepared at different relative densities (30%, 50%, and 70%) overlaying. The ultimate bearing capacity obtained for various combinations was used to develop the ANN model. The input parameters of the ANN model were thickness of recycled construction waste layer to diameter of circular footing ratio, angle of internal friction of sand, unit weight of sand, angle of internal friction of recycled construction waste and unit weight of recycled construction waste, and the model's output parameter was ultimate bearing capacity. The FANN-SIGMOD_SYMMETRIC model with topology 3-2-1 provided a higher estimate of the ultimate bearing capacity of circular footing, according to the ANN findings. The sensitivity analysis also revealed that the unit weight of sand and angle of internal friction of sand had insignificant effects on ultimate bearing capacity. The estimated ultimate bearing capacity was most affected by the angle of internal friction of recycled construction waste. The result of multiple linear regression analysis was not as good as the ANN model at predicting the ultimate bearing capacity.
