摘要
针对当前水利工程中岩爆灾害预测准确度不高的问题,提出一种基于RBF的水电站岩爆预测模型,利用该模型结合工程实例数据对岩爆灾害进行预测。在考虑岩爆产生的内外因基础上,选取应力系数、脆性系数以及弹性能量指数作为预测模型的输入变量。利用锦屏二级水电站实际工程数据对模型进行实验分析,结果表明该模型的预测结果与实际情况完全吻合,验证了模型的准确性和有效性。本研究为其他类似岩爆灾害的防治提供了科学依据。
Aiming at the problem of low accuracy of rockburst disaster prediction in current water conservancy projects,a RBF-based rockburst prediction model for hydropower stations is proposed.This model is combined with engineering example data to predict rockburst disasters.In considering the occurrence of rockburst.On the basis of external factors,the stress coefficient,brittleness coefficient and elastic energy index are selected as the input variables of the prediction model.The actual engineering data of Jinping n Hydropower Station is used to conduct experimental analysis on the model.The results show that the prediction results of the model are completely consistent with the actual situation,which verifies the accuracy and effectiveness of the model.This research provides a scientific basis for the prevention and control of other similar rockburst disasters.
作者
徐根祺
XU Genqi(Electric Engineering Department,Xi'an Traffic Enginering Institute,Xi'an 710300,China)
出处
《西安交通工程学院学术研究》
2021年第1期27-29,共3页
Academic Research of Xi'an Traffic Engineering Institute
关键词
水电站
RBF神经网络
岩爆预测
Hydropower station
RBF neural network
Rockburst prediction
作者简介
徐根祺(1984-),男,陕西西安人,硕士,工程师。主要研究方向:人工智能算法。E-mail:271427071@qq.com.
