Application of hybrid intelligent systems in predicting the unconfined compressive strength of clay material mixed with recycled additive

Abstract

A reliable prediction of the soil properties mixed with recycled material is considered as an ultimate goal of many geotechnical laboratory works. In this study, after planning and conducting a series of laboratory works, some basic properties of marine clay treated with recycled tiles together with their unconfined compressive strength (UCS) values were obtained. Then, these basic properties were selected as input variables to predict the UCS values through the use of two hybrid intelligent systems i.e., the neuro-swarm and the neuro-imperialism. Actually, in these systems, respectively, the weights and biases of the artificial neural network (ANN) were optimized using the particle swarm optimization (PSO) and imperialism competitive algorithm (ICA) to get a higher accuracy compared to a pre-developed ANN model. The best neuro-swarm and neuro-imperialism models were selected based on several parametric studies on the most important and effective parameters of PSO and ICA. Afterward, these models were evaluated according to several well-known performance indices. It was found that the neuro-swarm predictive model provides a higher level of accuracy in predicting the UCS of clay soil samples treated with recycled tiles. However, both hybrid predictive models can be used in practice to predict the UCS values for initial design of geotechnical structures.