Abstract
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 indicate that the six input parameters are effective in enabling the model to achieve a high prediction accuracy of 0.93, while also being straightforward to implement. The integrated model attained an R² of 0.925 for permeability coefficient prediction (MSE = 0.769, MAE = 0.623), and for compressive strength prediction, it reached an R² of 0.928 (MSE = 1.570, MAE = 0.910). This represents an improvement of 15.9-23.9% over the optimal single-model prediction accuracy. This enhancement can be attributed to the model's ability to effectively address the limitations posed by the linear assumptions of traditional empirical formulations through base-learner feature reweighting and meta-learner dynamic fusion mechanisms. Consequently, the ensemble model demonstrates significantly superior performance compared to empirical formulations in predicting both permeability and compressive strength. Notably, the compressive strength of pervious concrete is more sensitive to variations in porosity than to those in permeability.