摘要
爆破是土石坝料开采环节中最常用的方法之一。爆破块度不仅影响开采料的挖装效率,而且对坝体压实质量有很大影响,因此,通过调整爆破设计参数以控制开采料的粒度分布,是爆破实时控制的重要措施之一。在分析爆破参数对于块度影响基础上,针对传统爆破预测模型存在的不足,提出了一种基于双隐含层LM算法的神经网络模型预测爆破块度尺寸的方法。通过工程爆破试验实例,验证了该神经网络模型及计算方法的可行性及实用性,并用于指导工程需要。
Blasting is one of the most common methods for exploitation of rock-fill dam materials,and its fragmentation not only affects the excavation and loading efficiency of material mining,but has a great impact on the compaction quality of dam construction.Therefore,adjusting blast design parameters to control the fragment distribution of mining materials is a key measure for real-time blasting control.Aimed at the deficiency of traditional models in predicting blasting fragmentation,a LevenbergMarquardt(LM)algorithm based neural network model of two hidden layers is developed for the prediction.Through a case study of blast test fragmentation in a water conservancy project,the validity and practicability of this model and the method are verified.
作者
王仁超
吴松
WANG Renchao;WU Song(State Key Laboratory of Hydraulic Engineering Simulation and Safety,Tianjin University,Tianjin 300072)
出处
《水力发电学报》
EI
CSCD
北大核心
2019年第7期100-109,共10页
Journal of Hydroelectric Engineering
关键词
水利工程
爆破技术
坝料开采
爆破块度预测
神经网络
LM算法
hydraulic engineering
blasting technology
dam material excavation
blasting fragmentation prediction
neural network
LM algorithm
