防灾与环境

基于案例反分析的地下洞室围岩片帮深度预测方法

  • 刘志强 ,
  • 刘国锋 ,
  • 陈学琦 ,
  • 段淑倩 ,
  • 裴书锋
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  • 1.长安大学 公路学院,西安 710064;
    2.郑州大学 土木工程学院,郑州 450001;
    3.华北水利水电大学 地球科学与工程学院,郑州 450046
刘志强(2000—),男,河南平顶山人,硕士生,主要从事岩土工程灾害方面的科研工作。E-mail:zqliu0302@126.com
刘国锋(1989—),男,甘肃庆阳人,博士,副教授,主要从事岩土工程灾害机制、预测与防控方面的教学与研究工作。E-mail:gfliu@chd.edu.cn

收稿日期: 2025-08-07

  网络出版日期: 2026-06-23

基金资助

中央高校基本科研业务费专项资金(300102213203);国家自然科学基金(52209120);陕西省重点研发计划(2023KXJ-159);甘肃省科技重点研发计划(22YF11GA299)

Predicting Method of Rock Spalling around Deep Underground Caverns Based on Case Back Analysis

  • Liu Zhiqiang ,
  • Liu Guofeng ,
  • Chen Xueqi ,
  • Duan Shuqian ,
  • Pei Shufeng
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  • 1. School of Highway, Chang'an University, Xi'an 710064, P. R. China;
    2. School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China;
    3. College of Geosciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450046, P. R. China

Received date: 2025-08-07

  Online published: 2026-06-23

摘要

片帮作为高地应力大型地下洞室工程中普遍出现的一种围岩局部破坏现象,严重威胁工程稳定性和施工安全。依托我国西南地区的白鹤滩水电站地下厂房工程,利用数值仿真、参数反演、现场调研等手段,提出了一套基于案例反分析的片帮深度数值评价方法。首先,利用遗传-神经网络(GA-ANN)反演方法,获得了不同片帮区的关键岩体力学参数;其次,基于围岩破裂评价指标RFD分析开挖后围岩脆性破坏区的范围和深度,并与现场揭露片帮破坏进行对比,结果表明:91%以上的片帮破坏深度对应的PRFD阈值处于1.35~1.50,工程应用表明,该阈值对片帮深度的预测吻合率达88%。该研究可为深部地下工程岩体灾害破坏深度的预测提供支持。

本文引用格式

刘志强 , 刘国锋 , 陈学琦 , 段淑倩 , 裴书锋 . 基于案例反分析的地下洞室围岩片帮深度预测方法[J]. 地下空间与工程学报, 2026 , 22(3) : 1056 -1067 . DOI: 10.20174/j.JUSE.2026.03.31

Abstract

Rock spalling is a common phenomenon of local rock mass failure in large underground cavern projects under high geostress, which seriously threatens the stability of engineering and construction safety. Relying on the underground powerhouse projects on both the left and right banks of the Baihetan Hydropower Station in Southwest China, a numerical evaluation method for the depth of rock spalling based on case back analysis is proposed through the use of numerical simulation, parameter inversion, field testing, and case investigation. Firstly, by collecting and organizing field data, the typical distribution pattern of spalling during the excavation of the roof arch and sidewalls of the Baihetan left and right bank powerhouses is statistically analyzed. Secondly, by taking rock displacement and loosening zone depth as target variables, key rock mechanics parameters in different rock spalling segmentations are obtained through the combination of a large number of field test results and the genetic-neural network algorithm (GA-ANN). Thirdly, the numerical simulation and the evaluation index of rock fracture damage (RFD) are used to analyze the range and depth of the brittle failure zone of the surrounding rock after excavation based on the hard rock degradation model (RDM) applicable to deep rock engineering, and the results are compared with the observed rock spalling damage on-site. The results show that more than 91% of the rock spalling depths correspond to RFD thresholds ranging from 1.35 to 1.50. Finally, the threshold was used to analyze the excavation of the layer Ⅲ of the underground powerhouses on the left and right banks of the Baihetan Hydropower Station. The results showed that the predicted accuracy of the rock spalling failure depth reached over 88%, proving the good applicability of the model in practical engineering, indicating that the RFD can be used to effectively predict the depth of rock spalling failure. This study can provide important support for predicting the depth of rock mass failure in deep underground engineering.

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