Prediction of Gypseous Soil Settlement Using Artificial Neural Network (ANN)

Gypseous soil exhibits problematic geotechnical engineering properties as they expand, collapse, disperse, undergo excessive settlement, owns a distinct lack of strength, and it is soluble. has metastable structure, with dissolvable minerals minimal quantity clay binding the particles together. When gypseous unsaturated, are quite potent. subjected to increased wetness, however, excess water weakens or damages bonds, resulting in shear failure subsequent settlement. Estimating settlement shallow foundations on soils difficult topic that still not fully understood. It concluded artificial neural network (ANN) appeared be viable solution since been successfully used numerous prognosis applications engineering. In this research, precipitation values gypsum were predicted under influence applied load using an network. The study found model very good predicting convergence between real predict values.