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水力发电学报 ›› 2019, Vol. 38 ›› Issue (7): 100-109.doi: 10.11660/slfdxb.20190710

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基于LM算法的神经网络模型预测爆破块度

  

  • 出版日期:2019-07-25 发布日期:2019-07-25

Neural network model based prediction of fragmentation of blasting using the Levenberg-Marquardt algorithm

  • Online:2019-07-25 Published:2019-07-25

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。

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

Abstract: 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 Levenberg-Marquardt (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.

Key words: hydraulic engineering, blasting technology, dam material excavation, blasting fragmentation prediction, neural network, LM algorithm

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