摘要
针对新关角隧道二郎洞断层束地段在高地应力条件下的围岩变形问题,基于原岩地应力测试的地应力场拓展结果及其它主要影响因素,文章提出了围岩变形潜势的概念及其等级划分的标准,通过样本统计并采用神经网络算法获得了关角隧道的变形潜势分布图。结果表明,关角隧道岭脊段、高和极高地应力段、宽大断层以及断层束地段具有Ⅰ级、Ⅱ级和Ⅲ级大变形潜势,仅有部分地段属于常规变形潜势,凸显出关角隧道在复杂的软弱地层条件和极高地应力状态共同条件下具有显著的大变形潜势。通过较准确地判定关角隧道各段的变形潜势,经修正、优化设计和施工措施后,大部分监测结果被控制在常规变形范围内,其余均被控制在Ⅰ级大变形范围内,效果较好。该研究成果为解决类似条件下的围岩大变形问题提供了一种准确、实用的预测与控制方法。
Aiming at the surrounding rock deformation of the Erlangdong fault bundle at the location of the new Guanjiao tunnel under high geo-stress, a concept of surrounding rock deformation potential and its classification criterion are put forward based on the geo-stress field extension results from the original geo-stress test and the main influencing factors. The distribution of the deformation potential in the Guanjiao tunnel is obtained by sample statistics and the neural network algorithm. The results show that the ridge section, sections with high and extremely high geo-stress, large fault section and fault bundle section of the Guanjiao tunnel have a deformation potential of grades Ⅰ , Ⅱ and Ⅲ, and only partial sections have common deformation potential, which indicates the Guanjiao tunnel has large deformation potential under the complex conditions of soft rock and extremely high geo-stress. By determining the deformation potentials of each section in the Guanjiao tunnel, most of the monitoring results are controlled within the scope of common deformation after the means of correction, optimization design and construction, and the rest of the deformations are controlled within the scope of grade I. This achievement provides an accurate and practical forecasting and controlling method to solve the problem of large deformation in similar conditions.
作者
陈志敏
余云燕
李国良
赵德安
CHEN Zhimin;YU Yunyan;LI Guoliang;ZHAO De＇ an(Key Laboratory of Road ＆ Bridge and Underground Engineering of Gansu Province,Lanzhou Jiaotong University,Lanzhou 730070;National and Provincial Joint Engineering Laboratory of Road ＆ Bridge Disaster Prevention and Control,Lanzhou Jiaotong University,Lanzhou 730070;China Railway First Survey and Design Institute Group Ltd.,Xi＇ an 710043;School of Civil Engineering,Northwest University for Nationalities,Lanzhou,Gansu 730030)
出处
《现代隧道技术》
EI
CSCD
北大核心
2018年第4期33-41,共9页
Modern Tunnelling Technology
基金
国家自然科学基金(11662007)
长江学者和创新团队发展计划滚动资助(IRT_15R29)
关键词
高地应力
软弱围岩
关角隧道
神经网络
预测模型
变形潜势
High geo-stress
Soft rock
Guanjiao tunnel
Neural net
Forecast model
Deformation potential
