Prediction of Unconfined Compressive Strength of Expansive Soil Amended with Bagasse Ash and Lime Using Artificial Neural Network

Document Type : Regular Article


1 Research Scholar, Department of Civil Engineering, Malnad College of Engineering, Hassan, India

2 Professor, Department of Civil Engineering, Malnad College of Engineering, Hassan, India


Expansive soils (ES) have a long history of being difficult to work with in geotechnical engineering. Numerous studies have examined how bagasse ash (BA) and lime affect the unconfined compressive strength (UCS) of ES. Due to the complexities of this composite material, determining the UCS of stabilized ES using traditional methods such as empirical approaches and experimental methods is challenging. The use of artificial neural networks (ANN) for forecasting the UCS of stabilized soil has, however, been the subject of a few studies. This paper presents the results of using rigorous modelling techniques like ANN and multi-variable regression model (MVR) to examine the UCS of BA and a blend of BA-lime (BA + lime) stabilized ES. Laboratory tests were conducted for all dosages of BA and BA-lime admixed ES. 79 samples of data were gathered with various combinations of the experimental variables prepared and used in the construction of ANN and MVR models. The input variables for two models are seven parameters: BA percentage, lime percentage, liquid limit (LL), plastic limit (PL), shrinkage limit (SL), maximum dry density (MDD), and optimum moisture content (OMC), with the output variable being 28-day UCS. The ANN model prediction performance was compared to that of the MVR model. The models were evaluated and contrasted on the training dataset (70% data) and the testing dataset (30% residual data) using the coefficient of determination (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) criteria. The findings indicate that the ANN model can predict the UCS of stabilized ES with high accuracy. The relevance of various input factors was estimated via sensitivity analysis utilizing various methodologies. For both the training and testing data sets, the proposed model has an elevated R2 of 0.9999. It has a minimal MAE and RMSE value of 0.0042 and 0.0217 for training data and 0.0038 and 0.0104 for testing data. As a result, the generated model excels the MVR model in terms of UCS prediction.


Main Subjects

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