ESTIMATION OF THE ALLOWABLE BEARING CAPACITY OF SOIL IN SOME MUNICIPALITIES OF THE PROVINCE OF PAMPANGA USING ARTIFICIAL NEURAL NETWORK

Abstract

Urbanization is evident in some municipalities of Pampanga specifically in San Fernando and Santo Tomas. Those municipalities being developed, requires an information of the load-bearing capacity of soil. Predicting the soil bearing capacity provides an estimation of how much loads can the soil carry. The bearing capacity was calculated using the local shear failure equation of the Terzaghi’s bearing capacity formula. Also, the bearing capacity was predicted using Artificial Neural Network for those locations which has available data with the N60 value, friction angle, unit weight, and footing width as dependent parameters. The results show a coefficient of correlation of approximately equal to 1 and a mean squared error of at least 0 for a hidden layer of 10 which proves that ANN is an efficient way in predicting the bearing capacity. Based on the sensitivity analysis, it was found out that the unit weight is the most significant parameter affecting the value of the bearing capacity. A relationship between the N60 value, soil classification, and the bearing capacity was observed. It was concluded that the N60 value and soil classification are the two determining factors on how the value of the bearing capacity will be, because it affects the consistency of the soil and most of the parameters are dependent on those two variables. The value of the bearing capacity ranges from a minimum of 20 kPa to a maximum of 630 kPa for a specific area