Experimental study and predictive modelling of damping ratio in hybrid polymer concrete

Abstract

The improved damping capacity of concrete materials is crucial for many applications, especially the ones exposed to dynamic external forces. This study explores the optimal circumstances of copolymer composite binder, ratio of fine to coarse aggregate, curing time, size and content of tyre crumb rubber on enhancing damping ratio of hybrid polymer concrete. Owing to the limitation of conventional models and experimental methods in effectively handling the multiple and complicated relationship between the input parameters of the hybrid polymer concrete and its damping capacity, two robust machine leaning models including extreme gradient boosting (XGB), and artificial neural network (ANN) algorithms were developed to predict the values of damping ratio from an experimental database of 196 samples. Multiple linear regression (MLR) was employed in this study as a benchmark for comparing with the ANN and XGB methods. The results indicate that the considered algorithms yield accurate models for predicting the damping ratio of HPC composites. However, ANN and XGB outperform MLR with an impressive R-square of 0.985 and 0.981, respectively versus 0.875. Analysing the importance of input features using the XGB algorithm also reveals that the curing time has the highest impact on predicting the damping ratio. The volume fractions of resin matrix and crumb rubber are the next most important features. The crumb rubber size, within the studied range, appears to be the least impactful one.