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Application of coupled analysis methods for prediction of blast-induced dominant vibration frequency

ISSN号：1671-3664
期刊名称：《地震工程与工程振动：英文版》
时间：0
分类：O327[理学—一般力学与力学基础;理学—力学] TN914.41[电子电信—通信与信息系统;电子电信—信息与通信工程]
作者机构：[1]State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China, [2]China Nuclear Power Engineering Company Ltd., Shenzhen 518031, China
相关基金：National Natural Science Funds for Distinguished Young Scholar under Grant No. 51009086, Hubei Key Laboratory of Roadway Bridge and Structure Engineering under Grant No. DQJJ201313 and Major State Basic Research Development Program of China （973 Program） under Grant No. 2010CB732001

作者： Li Haibo[1], Li Xiaofeng[1], Li Jianchun[1], Xia Xiang[1], Wang Xiaowei[2]

关键词：频率预测, 开挖爆破, 耦合分析, 灰色关联度分析, 应用, 振型, 振动频率, 灰色关联分析, grey relational analysis, dimensional analysis, sensitivity analysis, dominant vibration frequency, prediction

中文摘要：

导致强风的主导的颤动频率(DVF ) 就岩石性质而言包含一个复杂、非线性、小的样品系统，爆炸参数和地形学。在这研究，为主导的颤动频率的预言的灰关系分析和维的分析过程的联合被介绍。六个因素基于灰关系分析从广泛的效果因素序列被选择，然后新奇导致强风的主导的颤动频率预言被维的分析获得。另外，预言被敏感分析与 195 个试验性的强风记录简化。确认基于爆炸的 firstperiod 的地点测试数据库为建议公式被执行在广东 Lufeng 核电厂(GLNPP ) 的挖掘。当时，结果证明建议途径有更高的适合度和更小的吝啬的错误与传统的预言相比。

英文摘要：

Blast-induced dominant vibration frequency （DVF） involves a complex, nonlinear and small sample system considering rock properties, blasting parameters and topography. In this study, a combination of grey relational analysis and dimensional analysis procedures for prediction of dominant vibration frequency are presented. Six factors are selected from extensive effect factor sequences based on grey relational analysis, and then a novel blast-induced dominant vibration frequency prediction is obtained by dimensional analysis. In addition, the prediction is simplified by sensitivity analysis with 195 experimental blast records. Validation is carried out for the proposed formula based on the site test database of the first- period blasting excavation in the Guangdong Lufeng Nuclear Power Plant （GLNPP）. The results show the proposed approach has a higher fitting degree and smaller mean error when compared with traditional predictions.

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