ANFIS Prediction Model for the Mechanical Properties of Soil and Activated Rice Husk Ash Blend for Sustainable Construction

Abstract

\n Adaptive neuro-fuzzy inference system (ANFIS), which integrates both Takagi-Sugeno fuzzy logic and neural network principles and also captures their benefits in a single framework was deployed for the modelling of the mechanical strength behaviour of expansive clayey soil treated with hydrated-lime activated rice husk ash (HARHA). The compaction properties, consistency limits and the activated ash (HARHA) were the predictors while CBR and UCS were the targets in this evolutionary model. The advantages of artificial intelligence techniques deployment in geotechnical research is to deal with the complex challenges associated with effectiveness in construction materials’ utilization so as to achieve optimal assessment of geotechnical materials’ behaviour and sustainable engineering design. ANFIS model development were carried out with 35 data sets derived from the experimental responses with respect to varying proportions of HARHA treatment from 0% to 12%. 25 and 10 datasets were used for training and testing the network respectively. The California bearing ratio (CBR) and unconfined compressive strength (UCS) were the target response while the HARHA replacement ratio, compaction and consistency limits properties were the input variables of the developed model. The model evaluation results obtained using statistical tools showed mean absolute error (MAE) of 0.582 and 0.7196, root mean square error (RMSE) of 0.6198 and 0.9004, mean square error (MSE) of 0.384 and 0.811, and coefficient of determination (CoD) value of 0.9973 and 0.9992 for CBR and UCS response parameters respectively. The results obtained indicates a very good performance in terms of prediction accuracy. This shows that ANFIS provides the flexibility in achieving sustainable geotechnical materials integration in civil works.