Document Type : Regular Article
Department of Civil Engineering, NIT Hamirpur, Himachal Pradesh, India
Department of Civil Engineering, M.tech student, NIT Hamirpur, Himachal Pradesh, India
Prediction of the free swell index of the expansive soil using artificial neural network has been presented in this paper. Input parameters for the artificial neural network model were plasticity index and shrinkage index, while the output was the free swell index. Artificial neural network algorithm used a back propagation model. Training of the artificial neural network model was conducted on the data collected from literature and the weights and biases were obtained which described the relation among the input variables and the output free swell index. Further, the sensitivity analysis was performed and the parameters affecting the free swell index of the expansive soil were identified. The sensitivity analysis results indicated that the plasticity index (63.97 %) followed by shrinkage index (36.03 %) was affecting the free swell index in this order. The study shows that the prediction accuracy of the free swell index of the expansive soil using artificial neural network model was quite good.