TY - EJOU
AU - Li, Fan
AU - Cai, Xin
AU - Zhang, Yanan
AU - Guo, Xingwen
AU - Jiang, Minmin
TI - Mechanical and Permeability Analysis and Optimization of Recycled Aggregate Pervious Concrete Based on Response Surface Method
T2 - Journal of Renewable Materials
PY -
VL -
IS -
SN - 2164-6341
AB - 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.
KW - Recycled aggregate pervious concrete (RAPC); response surface method (RSM); mechanical; permeability; optimization
DO - 10.32604/jrm.2022.024380