Predicting the Permeability of Pervious Concrete Based on the Beetle Antennae Search Algorithm and Random Forest Model

Pervious concrete is an environmentally friendly material that improves water permeability, skid resistance, and sound absorption characteristics. Permeability is the most important functional performance for the pervious concrete while limited studies have been conducted to predict permeability based on mix-design parameters. This study proposed a method to combine the beetle antennae search (BAS) and random forest (RF) algorithm to predict the permeability of pervious concrete. Based on the 36 samples designed in the laboratory and 4 key influencing variables, the permeability of pervious concrete can be obtained by varying mix-design parameters by RF. BAS algorithm was used to tune the hyperparameters of RF, which were then verified by the so-called 10-fold cross-validation. Furthermore, the model to combine the BAS and RF was validated by the correlation parameters. The results showed that the hyperparameters of RF can be tuned by the BAS efficiently; the BAS can combine the conventional RF algorithm to construct the evolved model to predict the permeability of pervious concrete; the cement/aggregate ratio was the most significant variable to determine the permeability, followed by the coarse aggregate proportions.