Water permeability prediction of sponge city pavement materials based on different machine learning algorithms

Abstract

Permeable pavement material is one of the most important supporting materials in the construction of sponge city, and its water permeability is the most important performance index. The water permeability test of permeable pavement materials is a tedious and complicated experimental work. It is of great research significance to predict the water permeability of permeable pavement materials through structural parameters modeling. In this paper, the database is first established by experimental means, and then the prediction models of LASSO (Least absolute shrinkage and selection operator), SVR (Support vector regression) and GBR (Gradient Boosting Regression) machine learning algorithms are established. Through the four factors of particle size, particle size distribution, shape parameters and binder content predict the water permeability of sponge city pavement materials. The results show that different machine learning algorithms have different sensitivity to the distribution of data samples. The fitting effect of GBR model water permeability prediction is better than that of SVR and LASSO models. The test value-predicted value MSE is 0.0051 and R2 is 0.92, which can effectively predict the water permeability of sponge city pavement materials.