Onyelowe, Kennedy C.Ebid, Ahmed M.Nwobia, Light I.2022-09-122022-09-122021Onyelowe, K. C., Ebid, A. M., & Nwobia, L. I. (2021). Predictive models of volumetric stability (durability) and erodibility of lateritic soil treated with different nanotextured bio-ashes with application of loss of strength on immersion; GP, ANN and EPR performance study. Cleaner Materials, 1, 100006. https://doi.org/10.1016/j.clema.2021.100006https://doi.org/10.1016/j.clema.2021.100006https://nru.uncst.go.ug/handle/123456789/4696Volumetric stability and erodibility are important soil properties influenced by moisture through raindrops and eventual runoff and the rise in water tables during wet seasons. Compacted subgrade materials made of clay respond to water ingress through swelling and shrinking in turn during drying and this poses a problem for foundation structures. Supplementary cementitious materials have been used to treat soils, in a cleaner procedure to improve the mechanical properties and to overcome undesirable behavior during changes in seasons. However, design and construction of foundation structures exposed to these problems become necessary and common, which requires constant visits to the laboratory and equipment needs. In order to overcome this, machine learning‐based predictive models have been proposed in this work for the estimation of durability (Sv) via loss of strength on immersion technique and erodibility (Er) of agro‐based ashes. Genetic programming (GP) (six levels of complexity), artificial neural network (ANN) (sigmoid activation function), evolutionary polynomial regression (EPR) (GA optimized PLR method) techniques have been used to conduct this intelligent prediction exercise. The performance of the models was conducted using the sum of squared errors (SSE) and coefficient of determination (R2) indices. The results show that EPR’s Er and Sv prediction with SSE of 5.1% and 2.7% respectively and R2 of 97.2% and 92.9% respectively outclassed GP and ANN. However, both GP and ANN showed minimal error and acceptable R2 above 0.85, which showed their ability to predict with good performance accuracy.enCleaner & green materialsGenetic programming (GP)Artificial neural network (ANN)Evolutionary polynomial regression (EPR)Nanotextured agro‐waste ashesErodibilityVolumetric stabilityTreated soilPredictive models performance (PMP)Article