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GUO Xiaosong, ZOU Chunxia, XUE Huijun, XU Deru, SUN Haoran, DING Feng. Mechanical Properties and Pore Characteristic of Alkali-Activated Aeolian Sand Concrete[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(11): 162-168. doi: 10.13204/j.gyjzG20122906
Citation: GUO Xiaosong, ZOU Chunxia, XUE Huijun, XU Deru, SUN Haoran, DING Feng. Mechanical Properties and Pore Characteristic of Alkali-Activated Aeolian Sand Concrete[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(11): 162-168. doi: 10.13204/j.gyjzG20122906

Mechanical Properties and Pore Characteristic of Alkali-Activated Aeolian Sand Concrete

doi: 10.13204/j.gyjzG20122906
  • Received Date: 2020-12-29
  • In order to explore the influence of alkali excitation on the mechanical properties of aeolian sand concrete under normal temperature curing, this study selected the Kubuqi Desert aeolian sand instead of river sand (40%), and using NaOH as the activator to configure alkali-activated aeolian sand concrete. Through compressive strength test and nuclear magnetic resonance test, the compressive strength and pore change law of aeolian sand concrete under normal temperature curing were studied. By introducing grey entropy correlation degree, the compressive strength model of alkali activated aeolian sand concrete was established. The results showed that under normal temperature curing, with the increase of NaOH content, the compressive strength of aeolian sand concrete first increased and then decreased. The porosity first decreased and then increased. The average increase rate of compressive strength of aeolian sand concrete was the highest when 5% NaOH was added, up to 51.02 MPa at 28 d, the pore structure was the best. The 28 d compressive strength of aeolian sand concrete replaced by 40% fly ash which was activated by 5% NaOH was equivalent to that of ordinary 20% fly ash aeolian sand concrete, and its pore structure was better, so as to effectively improve the utilization rate of fly ash and reduce the amount of cement. Furthermore, a predictive model of compressive strength GM(1,2) was established based on the gray entropy correlation degree. The average relative errors between predicted values and test values were 1.96% and 1.34%, with high accuracy.
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