Open Access

ARTICLE

Mechanical and Permeability Analysis and Optimization of Recycled Aggregate Pervious Concrete Based on Response Surface Method

Fan Li^1,#, Xin Cai^2,#, Yanan Zhang^1,*, Xingwen Guo^2,*, Minmin Jiang³

1 College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, 210000, China
2 College of Mechanics and Materials, Hohai University, Nanjing, 210000, China
3 China Three Gorges Corporation, Beijing, 100000, China

* Corresponding Authors: Yanan Zhang. Email: ; Xingwen Guo. Email:
# Fan Li and Xin Cai contributed equally to this work

Journal of Renewable Materials 2023, 11(4), 1745-1762. https://doi.org/10.32604/jrm.2022.024380

Received 31 May 2022; Accepted 21 July 2022; Issue published 01 December 2022

Abstract

In this paper, the effects of different influencing factors and factor interaction on the compressive strength and permeability of recycled aggregate pervious concrete (RAPC) were studied based on the response surface method (RSM). By selecting the maximum aggregate size, water cement ratio and target porosity as design variables, combined with laboratory tests and numerical analysis, the influences of three factors on the compressive strength and permeability coefficient of RAPC were revealed. The regression equation of compressive strength and permeability coefficient of recycled aggregate pervious concrete were established based on RSM, and the response surface model was optimized to determine the optimal ratio of RAPC under the conditions of meeting the mechanical and permeability properties. The results show that the mismatch item of the model is not significant, the model is credible, and the accuracy and reliability of the test are high, but the degree of uncorrelation between the test data and the model is not obvious. The sensitivity of the three factors to the compressive strength is water cement ratio > maximum coarse aggregate particle size > target porosity, and the sensitivity to the permeability coefficient is target porosity > maximum coarse aggregate particle size > water cement ratio. The absolute errors of the model prediction results and the model optimization results are 1.28 MPa and 0.19 mm/s, and the relative errors are 5.06% and 4.19%, respectively. With high accuracy, RSM can match the measured results of compressive strength and permeability coefficient of RAPC.

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Keywords

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