Comparative modelling of strength properties of hydrated-lime activated rice husk-ash (HARHA) modified soft soil for pavement construction purposes by artificial neural network (ANN) and fuzzy logic (FL) - Onyelowe, K. C.; Alaneme, G. U.; Onyia, M. E.; Bui Van, D.; Dimonyeka, M. U.; Nnadi, E.; Ogbonna, C.; Odum, L. O.; Aju, D. E.; Abel, C.; Udousoro, I. M.; Onukwugha, E. (Penerbit Universiti Kebangsaan Malaysia, 2021)
Artificial neural network and fuzzy logic based model soft-computing techniques were adapted in the research study for the evaluation of the expansive clay soil-HARHA mixture’s consistency limit, compressibility and mechanical strength properties. The problematic clay soil was stabilized with varying proportions of HARHA (stabilizing agent) which is an agricultural waste derivative from the milling of rice ranging from 0% to 12%; the utilization of the alkaline activated wastes encourages its recycling and re-use to obtain sustainable, eco-efficient and eco-friendly engineered infrastructure for use in the construction industry with economic benefits also. The obtained laboratory and experimental responses were taken as the system database for the ANN and fuzzy logic model development; the soil-HARHA proportions with their corresponding compaction and consistency limit characteristics were feed to the network as the model input variables while the mechanical strength (California-bearing-ratio (CBR), unconfined-compressive-strength (UCS) and Resistance value (R-values)) responses of the blended soil mixture were the model target variables. For the ANN model, feed forward back propagation and Levernberg Marquardt training algorithm were utilized for the model development with the optimized network architecture of 8-6-3 derived based on MSE performance criteria; while for the fuzzy logic model, the mamdani FIS with both triangular and trapezoidal membership function with both models formulated, simulated and computed using MATLAB toolbox. The models were compared in terms of accuracy of prediction using MAE, RMSE and coefficient of determination and from the computed results, 0.2750, 0.4154 and 0.9983 respectively for ANN model while 0.3737, 0.6654 and 0.9894 respectively was obtained for fuzzy logic model. The two models displayed robust characteristics and performed satisfactorily enabling the optimization of the solid waste derivatives utilization for soil mechanical properties improvement for engineering purposes.