1.宁夏大学;2.Research Group RecyCon, Department of Civil Engineering, KU Leuven
TU526；U414
无机固废协同制备绿色低碳建材技术研发与智能化应用示范
1.Ningxia University;2.Research Group RecyCon, Department of Civil Engineering, KU Leuven
为探究钢渣在不同细度下对钢渣-矿粉基地聚合物（SSG）流变性能影响，通过流动度、流变仪、水膜层厚度（water film thickness，WFT）试验研究不同细度钢渣流变性的变化规律，并基于流动度和WFT与流变参数的相关性，利用BP神经网络建立流变参数预测模型.结果表明，随着钢渣细度的提高，新拌地聚合物砂浆的流动性得到改善，并缩短其凝结时间和降低其WFT.钢渣细度的变化并未改变流体类型，流变特征符合Bingham模型，表观黏度随着剪切速率增大逐渐降低，屈服应力、塑性黏度、触变性随着钢渣细度增加持续降低，流变参数随着静置时间增加不断增大且增长率也呈逐渐增大趋势.同时，流动度与屈服应力成正相关，WFT与屈服应力、流动度存在着较好线性关系.建立的BP神经网络流变参数预测模型，预测结果吻合度良好、精度高.
In order to explore the influence of steel slags of different fineness on the rheological properties of the steel slag-mineral powder base polymer, the variation law of the rheology of different steel slag fineness was studied by fluidity, rheometer and water film thickness tests, and the rheological parameter prediction model was established by BP neural network based on the correlation between fluidity and WFT and rheological parameters. The results show that with the increase of steel slag fineness, the fluidity of the freshly mixed polymer mortar is improved, and its setting time and WFT are shortened. The change in slag fineness does not change the fluid type and the rheological characteristics are in line with the Bingham model. The apparent viscosity gradually decreases with the increase of shear rate, the yield stress, plastic viscosity and thixotropy continue to decrease with the increase of steel slag fineness, the rheological parameters increased with the increase of the standing time, and the growth rate also showed a gradual increasing trend. Meantime, the fluidity is positively correlated with the yield stress, and the WFT has a good linear relationship with the yield stress and fluidity. The BP neural network rheological parameter prediction model was established, and the prediction results were in good agreement and high accuracy.
