Comparative analysis of soft computing techniques in predicting the compressive and tensile strength of seashell containing concrete

Abstract

Despite the advantages of using seashells in concrete, predictive models have not yet been proposed for this type of concrete. To fill this gap, the present study utilized three distinctive machine learning models (i.e. Artificial neural network (ANN), Multivariate Adaptive Regression Spline (MARS), and M5P Model Tree) to investigate the compressive and tensile strength of seashell containing concrete using records collected from the technical literature. The results indicate that the ANN model outperforms the other developed models with R2 equal to 0.939 and 0.903 for the test dataset of compressive and tensile strength, respectively. Although the developed MARS and M5P models resulted in lower accuracy, they produced reliable predictions. Thus, the proposed models can be used successfully to estimate the mechanical properties of seashell-containing concrete. Also, sensitivity analysis of the best model follows experimental results highlighting the higher impact of cement, water and curing time parameters in all two types of strength. In terms of seashell content, the results confirmed that adding high amounts of the seashell to the concrete decreases its strength, while small amounts can be used without any significant adverse effect on strength parameters.