Predicting the permeability and compressive strength of pervious concrete using ensemble machine learning model

Developing the relationship of pore characteristics and performance is vital for predicting the properties of pervious concrete. However, the current performance prediction models mainly relied on porosity, ignoring the influence of other pore structure parameters, resulting in insufficient prediction accuracy. The aim of this paper is to establish machine learning-based models for predicting permeability and compressive strength of pervious concrete. Firstly, six independent models, the multiple linear regression and the Stacking algorithm were applied to construct the ensemble model. Secondly, 90 groups of pervious concrete specimens with varying porosities and grades were prepared and tested to obtain the initial data set. Then, the initial data set was augmented, and the prediction models were trained. The results show that 6 input parameters were suitable to make the models gain a high prediction accuracy (0.93) with simplicity. Compared to independent models, the ensemble models showed the highest accuracy and stability. This was due to that the ensemble models both utilized the basic training set and the output of the first level learner. The ensemble models performed significantly better than empirical formulas in predicting both permeability and compressive strength. Compared with the permeability, the compressive strength of pervious concrete is more sensitive to porosity.