Forecasting Shear Parameters, and Sensitivity and Error Analyses of Treated Subgrade Soil

There is an increasing dependence on laboratory testings in solving most of the environmental and highway subgrade failure problems. Thus, it has become necessary to forecast such problems beforehand making use of available machines and data in order to design and monitor future problems relying on predicted parameters. The present work has predicted the shear parameters of an unsaturated subgrade soil treated with hybrid cement and modified with nanotextured quarry fines using the learning algorithms of artificial neural network and group method of data handling, using multi-linear regression as a baseline regression. An A-7-6 group soil with high plasticity and undesirable for use as a subgrade foundation material was treated and multiple data were generated for several parameters, which served as independent variables in the modeling exercise. Predicted models were proposed and the models’ performance was conducted using common efficiency evaluation parameters. The performance evaluation showed that artificial neural network outclassed the other methods and with minimal error scale. However, artificial neural network and group method of data handling have proven their robustness and fitness in predicting subgrade engineering problems to be used in the design and performance monitoring of pavement infrastructures.