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

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

Abstract

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.

Keywords:

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

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References

B. Ferguson, Porous Pavements. Boca Raton, Florida, USA: CRC Press, 2005.

R. Zhong, Z. Leng, and C. Poon, "Research and application of pervious concrete as a sustainable pavement material: A state-of-the-art and state-of-the-practice review," Construction and Building Materials, vol. 183, pp. 544–553, Sep. 2018.

A. Abdelhady, L. Hui, and H. Zhang, "Comprehensive study to accurately predict the water permeability of pervious concrete using constant head method," Construction and Building Materials, vol. 308, Nov. 2021, Art. no. 125046.

X. Yang, J. Liu, H. Li, and Q. Ren, "Performance and ITZ of pervious concrete modified by vinyl acetate and ethylene copolymer dispersible powder," Construction and Building Materials, vol. 235, Feb. 2020, Art. no. 117532.

I. Y. Amir, A. M. Yusuf, and I. D. Uwanuakwa, "A Metaheuristic Approach of predicting the Dynamic Modulus in Asphalt Concrete," Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13106–13111, Apr. 2024.

A. Rezaei Lori, A. Bayat, and A. Azimi, "Influence of the replacement of fine copper slag aggregate on physical properties and abrasion resistance of pervious concrete," Road Materials and Pavement Design, vol. 22, no. 4, pp. 835–851.

H. Wang, H. Li, X. Liang, H. Zhou, N. Xie, and Z. Dai, "Investigation on the mechanical properties and environmental impacts of pervious concrete containing fly ash based on the cement-aggregate ratio," Construction and Building Materials, vol. 202, pp. 387–395, Mar. 2019.

H. Zhou, H. Li, A. Abdelhady, X. Liang, H. Wang, and B. Yang, "Experimental investigation on the effect of pore characteristics on clogging risk of pervious concrete based on CT scanning," Construction and Building Materials, vol. 212, pp. 130–139, Jul. 2019.

W. Yeih and J. J. Chang, "The influences of cement type and curing condition on properties of pervious concrete made with electric arc furnace slag as aggregates," Construction and Building Materials, vol. 197, pp. 813–820, Feb. 2019.

J. T. Kevern, D. Biddle, and Q. Cao, "Effects of Macrosynthetic Fibers on Pervious Concrete Properties," Journal of Materials in Civil Engineering, vol. 27, no. 9, Sep. 2015, Art. no. 06014031.

A. Ibrahim, E. Mahmoud, M. Yamin, and V. C. Patibandla, "Experimental study on Portland cement pervious concrete mechanical and hydrological properties," Construction and Building Materials, vol. 50, pp. 524–529, Jan. 2014.

"Report on Pervious Concrete ( Reapproved 2011 ) Reported by ACI Committee 522," American Concrete Institute (ACI), ACI 522 R10, 2010.

S. V. Thai, H. V. Viet, C. N. Tuan, A. T. D. Thao, and V. T. Bao, "Predicting the permeability of pervious concrete based on a data-driven approach," Transport and Communications Science Journal, vol. 73, no. 2, pp. 176–188, 2022.

F. Montes and L. Haselbach, "Measuring Hydraulic Conductivity in Pervious Concrete," Environmental Engineering Science, vol. 23, no. 6, pp. 960–969, Nov. 2006.

V.-H. Vu, B.-V. Tran, B.-A. Le, and H.-Q. Nguyen, "Prediction of the relationship between strength and porosity of pervious concrete: A micromechanical investigation," Mechanics Research Communications, vol. 118, Dec. 2021, Art. no. 103791.

B.-A. Le et al., "Predicting the Compressive Strength and the Effective Porosity of Pervious Concrete Using Machine Learning Methods," KSCE Journal of Civil Engineering, vol. 26, no. 11, pp. 4664–4679, Nov. 2022.

B.-A. Le, B.-V. Tran, T.-S. Vu, V.-H. Vu, and V.-H. Nguyen, "Predicting the Compressive Strength of Pervious Cement Concrete based on Fast Genetic Programming Method," Arabian Journal for Science and Engineering, vol. 49, no. 4, pp. 5487–5504, Apr. 2024.

J. R. Koza, "Genetic programming as a means for programming computers by natural selection," Statistics and Computing, vol. 4, no. 2, pp. 87–112, Jun. 1994.

B. Burlacu, G. Kronberger, and M. Kommenda, "Operon C++: an efficient genetic programming framework for symbolic regression," in Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, Cancun, Mexico, Jul. 2020, pp. 1562–1570.

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How to Cite

[1]
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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