基础理论与实验研究

一种高地应力条件下爆破开挖诱发振动峰值的预测模型

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  • 1. 三峡大学 水利与环境学院,湖北 宜昌 443002;2. 武汉大学 水资源与水电工程科学国家重点实验室,湖北 武汉 430072
范勇,男,1988年生,博士,讲师,主要从事岩石动力学的研究与教学工作。

收稿日期: 2015-07-09

  网络出版日期: 2018-06-05

基金资助

国家自然科学基金项目(No. 51609127);湖北省自然科学基金项目(No. 2016CFB238);国家重点基础发展计划(973)项目(No. 2011CB076354)。

A model for predicting vibration peak induced by blasting excavation under high in-situ stress

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  • 1. College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang, Hubei 443002, China; 2. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, Hubei 430072, China

Received date: 2015-07-09

  Online published: 2018-06-05

Supported by

This work was supported by the National Natural Science Foundation of China(51609127), the Natural Science Foundation of Hubei Province(2016CFB238), and the National Key Basic Research Project of China(973 Program) (2011CB076354).

摘要

高地应力条件下深部岩体爆破开挖过程中,炸药爆炸产生的能量和岩体开挖释放的应变能共同构成了振动的能量源。采用传统的基于单响药量的萨道夫斯基经验公式及其改进公式预测高地应力条件下爆破开挖诱发振动峰值精度不高。通过量纲分析,提出了一种基于能量平衡原理的振动峰值预测模型。结合锦屏二级深埋引水隧洞爆破试验,以上半洞实测振动数据为学习样本,训练模型;以下半洞实测振动数据为对比样本,检验模型。结果表明:与传统预测模型相比,预测模型具有较高的拟合相关系数和较低的预测均方根误差,可以更好地应用于高地应力条件下爆破开挖诱发振动峰值的预测。

本文引用格式

范 勇,卢文波 ,周宜红,冷振东,严 鹏, . 一种高地应力条件下爆破开挖诱发振动峰值的预测模型[J]. 岩土力学, 2017 , 38(4) : 1082 -1088 . DOI: 10.16285/j.rsm.2017.04.020

Abstract

During the blasting excavation of deep rock mass under high in-situ stress, energy sources of vibration are composed of energy produced by the detonation of explosive and strain energy released by excavated rock mass. The precision of the prediction of vibration peak induced by blasting excavation under high in-situ stress is reduced by using Sodev’s empirical formula and its improved formulas which are based on the charge per delay. On the basis of energy conservation, a model is proposed for predicting the vibration peak by using the method of dimension analysis. By incorporating with blasting field test in the diversion tunnel of Jinping Ⅱ hydropower station, the monitored vibration data of upper part tunnel is used as the learning sample to calibrate the model, and monitored vibration data of lower part tunnel is used as the contrast sample to test the model. Predicted results indicate that the proposed model has a higher fitting correlation coefficient and a lower root mean square error than traditional ones, and thus it can be well used to predict the vibration peak induced by blasting excavation under high in-situ stress.
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