(1. 北京科技大学 土木与资源工程学院，北京 100083；
2. 金川集团股份有限公司，金昌 737100)
摘 要: 为了确定超深铁矿胶结充填体强度模型，进行全尾砂胶凝材料配比的充填体强度正交试验，研究水泥熟料、脱硫石膏和工业芒硝对充填体强度性能的影响，并建立充填体强度预测模型。结果表明：充填体强度随水泥熟料添加量的增大而减小，随脱硫石膏和芒硝添加量的增大而增大。全尾砂胶凝材料最优配方：水泥熟料9%、脱硫石膏4%、芒硝1%。考虑井下作业时间，建立了养护龄期与其强度的双曲线函数关系，揭示随龄期增长强度先增加后趋于稳定的规律，为此提出极限抗压强度p1和修正参数p2的概念；采用1st Opt软件，极限抗压强度及修正参数与熟料添加量、石膏添加量及芒硝添加量呈多元非线性关系；综合胶凝材料添加量和养护龄期，建立充填体强度模型。模型预测误差小于6%，满足工程预测精度，可应用于全尾砂胶凝材料开发与强度预测。
关键字: 胶凝材料；正交试验；数据可视化；配比优化；强度模型
（1. School of Civil and Resource Engineering, University of Science and Technology of Beijing, Beijing 100083, China;
2. Jinchuan Group Co., Ltd., Jinchang 737100, China）
Abstract:In order to determine the strength model of cemented filling body in ultra-deep iron ore, orthogonal test of the strength of filling body with full tailings cementing material ratio was carried out. The effects of cement clinker, desulfurization gypsum and industrial mirabilite on the strength performance of filling body were studied, and the strength model was established. The results show the strength of filling body decreases with the increase of cement clinker content, and then increases with the increase of desulfurization gypsum and mirabilite content. Full tailings cementing material optimization formula is clinker 9%, gypsum 4%, mirabilite 1%. Considering downhole operation time, the hyperbolic function relationship between the curing age and its strength was established, which reveals that strength increases first and then tends to be stable with increasing age. The concepts of the ultimate compressive strength p1 and the revised parameter p2 are proposed. By 1st Opt software, the relationship between the conceptual parameters and the cementitious materials addition is multivariate and non-linear. The strength model was established by synthesizing cementitious materials addition and curing age. The prediction error of model is less than 6%, which satisfies the engineering prediction accuracy and can be applied to develop full tailings cementitious materials and predict strength.
Key words: cementitious materials; orthogonal experiment; data visualization; ratio optimization; strength model
