Onyelowe, Kennedy C.Iqbal, MudassirJalal, Fazal E.Onyia, Michael E.Onuoha, Ifeanyichukwu C.2022-09-122022-09-122021Onyelowe, K. C., Iqbal, M., Jalal, F. E., Onyia, M. E., & Onuoha, I. C. (2021). Application of 3-algorithm ANN programming to predict the strength performance of hydrated-lime activated rice husk ash treated soil. Multiscale and Multidisciplinary Modeling, Experiments and Design, 4(4), 259-274. https://doi.org/10.1007/s41939-021-00093-7https://doi.org/10.1007/s41939-021-00093-7https://nru.uncst.go.ug/handle/123456789/4673Artificial neural network (ANN) method has been applied in the present work to predict the California bearing ratio (CBR), unconfined compressive strength (UCS), and resistance value (R) of expansive soil treated with recycled and activated composites of rice husk ash. Pavement foundations suffer from poor design and construction, poor material handling and utilization and management lapses. The evolutions of soft computing techniques have produced various algorithms developed to overcome certain lapses in performance. Three of such algorithms from ANN are Levenberg–Muarquardt Backpropagation (LMBP), Bayesian Programming (BP), and Conjugate Gradient (CG) algorithms. In this work, the expansive soil classified as A-7-6 group soil was treated with hydrated-lime activated rice husk ash (HARHA) in varying proportions between 0.1 and 12% by weight of soil at the rate of 0.1% to produce 121 datasets. These were used to predict the behavior of the soil’s strength parameters (CBR, UCS and R) utilizing the evolutionary hybrid algorithms of ANN. The predictor parameters were HARHA, liquid limit (wL), (plastic limit (wP), plasticity index (IP), optimum moisture content (wOMC), clay activity (AC), and (maximum dry density (δmax). A multiple linear regression (MLR) was also conducted on the datasets in addition to ANN to serve as a check and linear validation mechanism. MLR and ANN methods agreed in terms of performance and fit at the end of computing and iteration. However, the response validation on the predicted models showed a good correlation above 0.9 and a great performance index. Comparatively, the LMBP algorithm yielded an accurate estimation of the results in lesser iterations than the Bayesian and the CG algorithms, while the Bayesian technique produced the best result with the required number of iterations to minimize the error. And finally, the LMBP algorithm outclassed the other two algorithms in terms of the predicted models’ accuracy.enSoft computingArtificial intelligenceArtificial neural network (ANN)Machine learning in geotechnicsBack-propagation algorithmLevenberg–muarquardt algorithmBayesian algorithmConjugate gradient algorithmSustainable construction materialsArticle