Preparation and Performance Study of Novel Mineral Sea Rock Concrete
In order to solve the shortage of fresh water,river sand and gravel in marine engineering,Mineral Sea Rock Concrete(MSRC)was prepared using sea sand and coral as aggregate and seawater as mixing water,and pre-wetting the coral first,and then mixing it with cement mortar.The effects of net water-cement ratio,aggregate water content and sand rate on the compatibility and compressive strength of MSRC were analyzed by slump and compressive strength tests,respectively.The results showed that the slump increased with increasing sand ratio in the interval of 45%～58%,and decreased with increasing sand ratio in the interval of 58%～60%.When the water content of coral aggregate is 12%,the mixed concrete has the best workability and the highest cube compressive strength.The cube compressive strength with a net water-cement ratio of 0.43,a sand rate of 47%,and an aggregate moisture content of 12%is 3.9%and 10.6%higher than that of aggregates with a moisture content of 9%and 15%,respectively.When the strength grades are the same,the cube compressive strength increases first and then decreases as the sand ratio increases.For the mix proportions with strength grades of MSRC20,MSRC25 and MSRC30,the maximum cube compressive strength was achieved when the net water-cement ratio at 0.43,0.41 and 0.30,respectively.The compressive strength of MSRC develops rapidly in the early stage,and the growth rate gradually slows down after 15 d.Based on the test results,the formula for calculating the cube compressive strength of MSRC is proposed,and the comparison of the test results and the formula calculation results shows that the proposed formula matches well with the test results.The MSRC prepared by the author has good workability and mechanical properties.The research results can provide important basis for the mix proportions design and engineering application of MSRC.
mineral sea rock concretecompressive strengthnet water-cement ratiosand ratiowater content of coral aggregate
万方数据
维普
