钢渣对矿渣基高水充填材料性能的影响及强度预测  

Influence of Steel Slag Content on Portland Based High-water Backfilling Materials and Strength Forecasting

作　　者：董越[1] 杨志强[1,2] 高谦[1] 

机构地区：[1]北京科技大学金属矿山高效开采与安全教育部重点试验室,北京100083 [2]金川集团股份有限公司镍钴资源综合利用国家重点实验室,金昌737100

出　　处：《硅酸盐通报》2017年第11期3841-3847,共7页Bulletin of the Chinese Ceramic Society

基　　金：国家高技术研究发展计划(863计划)(SS2012AA062405)

摘　　要：采用钢渣、矿渣和铁尾砂等固体废弃物制备矿渣基高水充填材料,通过对其正交试验样本建立BP神经网络强度预测模型,并结合实测强度预测出不同龄期15%和25%钢渣掺量高水充填材料的抗压强度,分析了钢渣掺量对高水充填材料物理力学性能的影响,不同龄期高水充填材料水化产物的变化规律。研究表明:通过对正交试验样本建立的BP神经网络强度预测模型,可实现对各龄期不同钢渣掺量的高水充填材料抗压强度的预测;随着钢渣掺量的升高,高水充填材料的强度逐渐下降,吸水率逐渐增大,膨胀收缩率先减小后升高;随着养护龄期的延长,水化产物中的石英、氢氧化钙和斜硅钙石的衍射峰逐渐钝化,方解石、水化硅酸钙、C-S-H(I)和羟镁铝石晶相的衍射峰逐渐锐利。The steel slag,portland slag,iron tailings and other solid waste were utilized for portland based high-water backing materials. With the establishment of strength prediction model of BP neural network with orthogonal test samples, the compressive strength of 15% and 25% steel slag content were predicted,the influence of steel slag content on mechanical performance of high-water backing materials was analyzed,the influence on hydration products in different curing periods was analyzed. The results show that the model of BP neural network with orthogonal test samples could be used to predict strength;with the content of steel slag increased,the compressive strength of samples reduced,water absorption increased,shrinkage rate increased after decreased; with curing periods increased,the diffraction peaks of quartz,portlandite and larnite became passivated,the diffraction peaks of calcite,hydrocalumite,C-SH（I） and meixnerite became sharp.

关 键 词：钢渣基 高水充填材料 力学性能 水化产物 

分 类 号：TD861[矿业工程—金属矿开采]