工程科学学报2016，Vol.38Issue(8)：1059-1068,10.DOI:10.13374/j.issn2095-9389.2016.08.003
基于超声波波速及 BP 神经网络的胶结充填体强度预测
Prediction of cemented backfill strength by ultrasonic pulse velocity and BP neural network
摘要
Abstract
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.关键词
充填/抗压强度/预测模型/超声波波速/神经网络Key words
backfilling/compressive strength/prediction models/ultrasonic pulse velocity/neutral networks分类
矿业与冶金引用本文复制引用
徐淼斐,高永涛,金爱兵,周喻,郭利杰,刘光生..基于超声波波速及 BP 神经网络的胶结充填体强度预测[J].工程科学学报,2016,38(8):1059-1068,10.基金项目
国家自然科学基金资助项目(51174014) （51174014）
科技北京百名领军人才培养工程资助项目(Z151100000315014) （Z151100000315014）
