This research study presents modelling of the experimental results which evaluates the effects of RHA and PWS on concrete’s compressive strength after 28-day hydration period using ANFIS to achieve sustainable engineering works. The benefits of soft computing applications for structural materials optimization are to deal with complex problems associated with systematically incorporating admixtures in concrete production to achieve sustainable and durable engineering design. The ANFIS model was developed using 62 datasets gotten from the laboratory results with respect to varying ratios of replacement of cement and fine aggregates with RHA and PWS, respectively, from 0 to 50%. The ANFIS computation toolbox in MATLAB software was utilized for the model simulation, testing, training and validation of the mechanical behaviour of green concrete produced with varying proportions of RHA and PWS as admixture using hybrid method of optimization and grid partition method of FIS at 100 Epochs. The mixture variations of cement–RHA and fine-aggregatePWS combinations were taken as the input parameters while the output variable is the compressive strength response. The ANFIS model performance evaluation results obtained using loss function parameters showed MAE of 0.2244, RMSE of 0.625, MSE of 0.3906, and coefficient of determination value of 98.67% which indicates a good relationship between the predicted and actual results.
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Alaneme, G.U., Mbadike, E.M., Iro, U.I. et al. Adaptive neuro-fuzzy inference system prediction model for the mechanical behaviour of rice husk ash and periwinkle shell concrete blend for sustainable construction. Asian J Civ Eng 22, 959–974 (2021). https://doi.org/10.1007/s42107-021-00357-0
- Supplementary cementitious materials (SCM)
- Fuzzy logic (FL)
- Artificial neural networks (ANN)
- Concrete compressive strength properties