基于正交试验的粉煤灰-硅锰渣再生混凝土力学性能研究

为研究固体废弃物取代混凝土原材料的适用性及对混凝土性能的影响,对16组粉煤灰-硅锰渣再生混凝土试块进行正交试验,研究当粉煤灰体积取代胶凝材料、硅锰渣体积取代砂、再生骨料质量取代粗骨料时不同取代量对混凝土坍落度、立方体抗压强度和劈裂抗拉强度的影响。结果表明:当粉煤灰取代量为40%(体积分数)时,混凝土坍落度提高率最大,为14.5%,立方体抗压强度和劈裂抗拉强度均降低,降低率分别为7.2%、22.8%;硅锰渣的取代会降低混凝土坍落度,当硅锰渣取代量为80%(体积分数)时,立方体抗压强度和劈裂抗拉强度的降低率最小,分别为0.5%、11.5%;再生骨料的取代会降低混凝土坍落度,当再生骨料取代量为100%(质量分数)时,立方体抗压强度的降低率为1.9%,劈裂抗拉强度的降低率为12.4%。通过优化模型NSGM(1,4)对混凝土立方体抗压强度进行模型预测,模型模拟平均相对误差为0.542%,模型预测平均相对误差为2.727%。

Mechanical Properties of Fly Ash-Silicon Manganese Slag Recycled Concrete Based on Orthogonal Test

To study the applicability of solid waste replacement of concrete raw materials and the effect on concrete properties, through the orthogonal test of 16 groups of fly ash-silica manganese slag recycled concrete test blocks, the effects of fly ash volume substitution for cementitious material, silicon manganese slag volume substitution for sand, and recycled aggregate mass substitution for coarse aggregate on concrete slump, cube compressive strength and splitting tensile strength were studied under different substitution amounts. The results show that when the substitution amount of fly ash is 40% (volume fraction), theincreasing rate of concrete slump is the largest, which is 14.5%, and the cube compressive strength and splitting tensile strength are reduced, and the reduction rate are 7.2% and 22.8%, respectively. The substitution of silicon manganese slag reduces the slump of concrete, and when the substitution amount of silicon manganese slag is 80% (volume fraction), the reduction rate of the compressive strength and splitting tensile strength is the smallest, which are 0.5% and 11.5%, respectively. The substitution of recycled aggregate reduces the slump of concrete, and when the substitution amount of recycled aggregate is 100% (mass fraction), the reduction rate of the cube compressive strength is 1.9%, and the reduction rate of splitting tensile strength is 12.4%. The cube compressive strength of concrete is predicted by the optimized model NSGM(1,4). The average relative error of model simulation is 0.542%, and the average relative error of model prediction is 2.727%.