【摘要】 在测试磷石膏充填材料物理化学性能的基础上,测定了不同配比充填体的强度,用分形理论揭示了磷石膏充填体强度特征。研究表明,磷石膏充填体强度与水泥含量、料浆浓度、粒径分形维数、孔隙分形维数及分维数相关率相关。根据充填体强度影响因素,建立了磷石膏加粉煤灰作为充填料的充填体强度预测的BP神经网络模型。同时利用遗传算法优化BP神经网络的学习过程,验算结果显示,GA-BP神经网络模型预测误差在4%以内,具有较高的预测精度。更多还原

【Abstract】 After testing physical and chemical properties of phosphogypsum backfill material,the strengths of backfill with different proportioning were obtained from experiments,and the strength characteristics of phosphogypsum backfill were revealed by fractal theory.Researches show that the strengths of phosphogypsum backfill are related to the content of cement,the concentration of slurry,the fractal dimension of particle,the fractal dimension of porosity and the correlation coefficient of fractal dimension.Based on the influencing factors of backfill strength,the BP neural network model was established to predict strengths of backfill by using phosphogypsum and coal ash as backfill materials.A genetic algorithm was adopted to optimize the BP neural network learning process.The calculation result shows GA-BP neural network model has higher prediction precision,with errors less than 4%.更多还原