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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.
Highlights
To solve the problem of traditional cities, the concept of "sponge city" came into being in the era of water shortage, water quality pollution, urban waterlogging and other urban water problems in various countries around the world
When the single gradation A sand is mixed into the continuous gradation L sand, the finer A sand is filled into the void of L sand when the amount of incorporation is small, so the stacking structure is more compact and the water permeability rate is reduced to the minimum; When a large amount of A sand incorporation becomes the dominant aggregate, the standard deviation decreases, the particle size distribution becomes narrow, the porosity increases, so the water permeability rate increases
In this paper, starting from the characteristic performance parameters of sponge city pavement materials, water permeability prediction models based on three machine learning algorithms of LASSO, SVR and GBR is constructed
Summary
To solve the problem of traditional cities, the concept of "sponge city" came into being in the era of water shortage, water quality pollution, urban waterlogging and other urban water problems in various countries around the world. In view of the many factors affecting the performance of sponge city pavement materials and their non-linear laws, the model based on the three machine learning algorithms of LASSO, SVR, and GBR was selected for the prediction study of the performance of sponge city pavement materials. The training samples they require are few and have high accuracy. Through the four performance parameters of particle size, particle size distribution, shape parameter and binder content, water permeability prediction is performed, and the prediction accuracy of the model is compared and evaluated
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