Neuro-swarm and neuro-imperialism techniques to investigate the compressive strength of concrete constructed by freshwater and magnetic salty water

Abstract

To construct concrete in recent decades, the reducing freshwater is one of the basic requirements. Feasibility of replacing freshwater with seawater has been confirmed in literature. This study aims to investigate the effects of fresh and salty water on the compressive strength of the concrete samples after 28 days. Then, two hybrid artificial intelligence techniques namely neuro-swarm and neuro-imperialism are proposed to predict the concrete compressive strength. In these two hybrid models, the particle swarm optimization and imperialist competitive algorithm were used to optimize the weights and biases of the artificial neural network to get a higher performance prediction results. For the purpose of this study, a series of experiments were carried out in laboratory based on different values of cement content, magnetic field intensity, water rotation time, and water to cement ratio. According to the results obtained in laboratory, the mentioned parameters had a deep impact on compressive strength of concrete samples. Therefore, these parameters were set as model inputs to estimate the compressive strength of concrete samples. Several hybrid intelligent models with different effective parameters were built to obtain the most accurate model in estimating compressive strength of concrete. The proposed models were assessed using some statistical indices e.g., system error and coefficient of determination (R2). As a result, both hybrid intelligence models were able to provide a high accuracy level for predicting compressive strength of concrete. However, neuro-swarm model received a better results compared to neuro-imperialism model. Considering R2 values for train and test phases, values of (0.9811 and 0.9668) and (0.9785 and 0.9706) were obtained for the neuro-swarm and the neuro-imperialism models, respectively which confirmed the better performance of the neuro-swarm intelligence model in predicting compressive strength values of concrete samples.