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Properties and relationships of porous concrete based on Griffith’s theory: compressive strength, permeability coefficient, and porosity

  • Fujin Hou,
  • Guanglei Qu,
  • Zongwei Yan,
  • Mulian Zheng,
  • Ya Ma,
  • Jun Li,
  • Feifei Fan,
  • Jingang Zhang

摘要

Porous concrete (PC) is considered a promising paving material due to its eco-friendly and multi-functional characteristics. Compressive strength and coefficient of permeability are two key performance parameters of PC, but limited research has been conducted so far on their mutual relationship. In this paper, PC with three target porosities (15%, 20% and 25%) were prepared and 10 water–cement ratios between 0.2 and 0.4 were designed for each target porosity. After the samples were cured, the porosity and permeability coefficients were tested, followed by the compressive strength test. The porosity, permeability coefficient, and compressive strength characteristics of PC and their interrelationships were analyzed based on the test results. Moreover, a mathematical model is developed to characterize the relationship between compressive strength and permeability coefficient by analyzing empirical results and theoretical derivations. The results show that the effective porosity approaches the target porosity when the water–binder ratio (w/b) is in the range of 0.26–0.34. The strength and permeability of the PC can be both maintained at a high level when the effective porosity is in the range of 18–21%. Specifically, the PC may lose its water permeability by sealing the bottom with a w/b of 0.4 or more. Moreover, a new empirical model for the compressive strength and permeability coefficient of PC is established based on Griffith’s fracture theory. The model proposed presents a better agreement with the experimental data and could provide a better prediction of the compressive strength of PC by selecting the appropriate parameters. This research enriches the performance prediction model of PC and provides a basis and reference for PC material design and objective optimization.