基于超声波波速及 BP 神经网络的胶结充填体强度预测

尾砂胶结充填体作为一种水泥基多相复合材料，其单轴抗压强度与超声波波速受水泥含量、固体质量分数、试件形态等因素影响.通过制备三种形态(7.07 cm×7.07 cm×7.07 cm立方体，φ5 cm×10 cm圆柱体和φ7 cm×14 cm圆柱体)的试件并进行单轴抗压强度试验和声波波速测试，对充填体强度和波速受水泥含量、固体质量分数和试件形态影响的规律进行了灰色-关联度分析.结果表明：水泥含量是影响强度的关键核心因素，关联度为0.837；固体质量分数是影响波速的关键核心因素，关联度为0.712.建立了充填体强度-波速指数函数预测模型和BP神经网络预测模型，通过对两种预测模型进行统计分析的F检验和t检验验证了两种方法在充填体强度预测的可行性，为胶结充填体的强度预测提供了新方法.

Prediction of cemented backfill strength by ultrasonic pulse velocity and BP neural network

Tailing-cemented backfill is a cement-based heterogeneous composite whose uniaxial compressive strength ( UCS) and ultrasonic pulse velocity ( UPV) are dependent on cement dosage, solid content, sample type, etc. In this paper, uniaxial compres-sive test and ultrasonic pulse velocity test of three types of backfill samples (7. 07 cmí7. 07 cmí7. 07 cm cube,φ5 cmí10 cm cylin-der and φ7 cmí14 cm cylinder) were performed, and the effects of cement dosage, solid content and sample type on the backfill strength and ultrasonic pulse velocity were investigated by grey correlative degree analysis. The results show that cement dosage is the key to the backfill strength with a correlative degree of 0. 837, while the ultrasonic pulse velocity is mostly influenced by solid content with a correlation degree of 0. 712. An exponential prediction relation between UCS and UPV and a BP neural network prediction model were built, and they were validated by F-test and t-test of statistical analysis, respectively. The methods proposed can be new approaches for predicting the backfill strength.