Implementation of Artificial Neural Network for Forecasting California Bearing Ratio of Treated Cement-Laterite Soil Improved with Bamboo Leaf Ash

Finding the California Bearing Ratio (CBR) of soil stabilised by an environmentally friendly binder composite is one of the most important steps in designing an appropriate mix.By utilising an artificial neural network (ANN) to forecast soil parameters and additions of Portland cement and Bamboo Leaf Ash (BLA), this study aims to estimate the California Bearing Ratio (CBR) of treated cement-lateritic soils.The precise and accurate findings are obtained by selecting six factors as input variables.Maximum Dry Density (MDD) (kg/m 3 ), Plasticity Index (PI) (%), Liquid Limit (LL) (%), Cement (%), Bamboo Leaf Ash (BLA) (%), and OMC (%) were the six input variables.In contrast, the output variables were CBR soaked (%) and CBR unsoaked (%).1288 samples from a database were used in the investigation.Training is done using a multilayer perceptronbackpropagation algorithm.The network topology is acquired after the fixing of several hidden neurones.With a 99.5% accuracy rate, the model can predict CBR results.