A Genetic Programming-Assisted Analytical Formula for Predicting the Permeability of Pervious Concrete


Volume: 14 | Issue: 3 | Pages: 14775-14780 | June 2024 | https://doi.org/10.48084/etasr.7619


This study proposes a new approach to construct predictive formulas for the permeability of Pervious Concrete (PC), which depends on PC mixture and porosity. To achieve this, a dataset of 195 samples collected from different sources was used. In the dataset the permeability is dependent on porosity, aggregate-to-cement ratio (AC), maximum nominal sizes (MS) of coarse aggregate, and water-to-cement or binder ratios (WC). From the dataset and through applying simple regression techniques, several analytical functions based on the Kozeny-Carman model were constructed and evaluated for their effectiveness in implementing independent datasets and similar analytical functions. Furthermore, for the first time, the Genetic Programming-based Symbolic Regression method was adopted to construct hybrid models combined with the Kozeny-Carman analytical model. The equation of the hybrid model ensures both basic physical conditions and efficiency while being simple enough for engineering-level applications.


symbolic regression, genetic programming, permeability, machine learning, pervious concrete


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How to Cite

B.-A. Le, T. S. Vu, H.-Q. Nguyen, and V. H. Vu, “A Genetic Programming-Assisted Analytical Formula for Predicting the Permeability of Pervious Concrete”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14775–14780, Jun. 2024.


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