低模数水玻璃对橡胶轻骨料混凝土力学性能的影响

为了了解不同掺量水玻璃对橡胶轻骨料混凝土(RLC)力学性能的影响,在RLC中加入水玻璃,并用20％粉煤灰代替水泥,制作水玻璃掺量分别为0,2％,4％,6％,8％的混凝土试块.通过核磁共振试验研究其微观孔隙变化及强度形成机理;进行对数函数曲线预测和BP神经网络强度预测,判断其可靠性并对比优劣.结果表明:水玻璃的掺入使RL...

Effect of low modulus sodium silicate on mechanical properties of rubber lightweight aggregate concrete

In order to understand the influence of different dosages of sodium silicate on the mechanical performance of Rubber Lightweight Concrete(RLC), sodium silicate was added into rubber lightweight aggregate concrete(RLC)and 20% fly ash was used instead of cement. The concrete blocks with content of 0, 2%, 4%, 6% and 8% sodium silicate were prepared. Through NMR to study the microscopic change and strength formation mechanism. The logarithmic function curve prediction and BP neural network strength prediction were carried out to judge the reliability and compare the advantages and disadvantages. The results show that the compressive strength of RLC increases first and then decreases with the addition of sodium silicate, and the best content of sodium silicate is 2%. The mechanical properties of 80-mesh RLC are almost the same as the optimal group(20-mesh RLC group)with 28 d, and the mechanical properties can be improved by adding appropriate amount of sodium silicate. After the addition of sodium silicate, the porosity shows a tendency of first decreasing and then increasing, which indicates that the addition of appropriate content of sodium silicate can optimize the pore structure of concrete and improve the strength of concrete. The prediction models of curve fitting and BP Neural Network are established. Both models can be used to predict the concrete age strength, but the stability, reliability and comprehensiveness of parameters of the BP-neural network prediction model are better than those of curve fitting models.