Abstract
Pervious concrete (PC) is at risk of clogging due to the continuous blockage of sand into it during its service time. This study aims to evaluate and predict such clogging behavior of PC using hybrid machine learning techniques. Based on the 84 groups of the dataset developed in the earlier study, the clogging behavior of the PC was determined by the algorithm combing the SVM (support vector machines) and particle swarm optimization (PSO) methods. The PSO algorithm was employed to adjust the hyperparameters of the SVM and verify the performance using 10-fold cross-validation. The predicting results of the developed model were assessed by the coefficient of determination (R) and root mean square error (RMSE). The importance of the influential variables on the clogging behavior of PC was evaluated as well. The results showed that the PSO algorithm can effectively adjust the hyperparameters of the SVM model and can be used to construct the predictive model for the clogging behavior of the PC. The combined algorithm has the advantage of higher reliability and validity than the random hyperparameters selection. For the verification process, the developed model was able to obtain values of 0.9469 and 1.8148 for the R and RMSE, showing that the developed machine learning model can accurately be used to evaluate and predict the clogging behavior of the PC, guiding the mix-design of PC from the perspective of durability. The size of the clogging sand is the most important parameter and the thickness of the sample is the least significant factor affecting the clogging behavior. The proportions of the smallest aggregate size and largest aggregate size are the two most important design parameters of concrete with the consideration of the relatively higher importance scores, showing these two aggregates should be given special attention in future PC design for anti-clogging purposes.
Highlights
As the pervious concrete (PC) is playing its role of infiltration, it is inevitable that some small particles such as dust, sand, and gravel can penetrate the PC with the water
4.1 Performance of the particle swarm optimization (PSO) Algorithm To determine the best form of the support vector machines (SVM) model, the hyperparameters were tuned by the 10-fold cross-validation by evaluating the root mean square error (RMSE) values obtained from the testing dataset
The hyperparameter obtained at the tenth fold was used in this study to construct the SVM model for the permeability prediction of PC
Summary
With the gradual development of the economy and society, China is vigorously promoting the process of environmental protection and striving to build “sponge cities” to build a harmonious relationship between humans and nature [1–7]. To improve the atmospheric circulation and urban environment, pervious concrete (PC) was proposed as a new type of green construction material [11,12]. As a kind of new construction material, it is gradually showing its development advantages: (1) on the one hand, by laying pervious concrete, the surface runoff after heavy rain can penetrate the ground and reduce the pressure on municipal drainage facilities; (2) on the other hand, pervious concrete can penetrate the surface water into the soil to supplement the moisture in the soil and regulate the urban ecological environment [13–17]. It was found that the permeability of the PC was reduced by 90% after it was put into use for 5 years, resulting in impervious pavement that is unable to withstand urban flooding and freeze-thaw damage [13,18,19]
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