Three-dimensional intelligent inversion method for in-situ stress field based on SLR-ANN algorithm

ZHANG She-rong; HU An-kui; WANG Chao; PENG Zhen-hui

doi:10.16285/j.rsm.2017.09.035

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Three-dimensional intelligent inversion method for in-situ stress field based on SLR-ANN algorithm

更新时间：2026-06-25

- Three-dimensional intelligent inversion method for in-situ stress field based on SLR-ANN algorithm
- Vol. 38, Issue 9, Pages: 2737-2745(2017)
- 作者机构：
  
  1. 天津大学水利工程仿真与安全国家重点实验室,天津,300072
  2. 天津大学建筑工程学院,天津,300072
  3. 河海大学水文水资源与水利工程科学国家重点实验室,江苏,南京,210098
- 作者简介：
- 基金信息：
- DOI：10.16285/j.rsm.2017.09.035
  CLC： TV731+.6
- Online First：05 June 2018，
  
  Published：2017
- 稿件说明：
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ZHANG She-rong, HU An-kui, WANG Chao, et al. Three-dimensional intelligent inversion method for in-situ stress field based on SLR-ANN algorithm[J]. 2017, 38(9): 2737-2745.
DOI：

ZHANG She-rong, HU An-kui, WANG Chao, et al. Three-dimensional intelligent inversion method for in-situ stress field based on SLR-ANN algorithm[J]. 2017, 38(9): 2737-2745. DOI： 10.16285/j.rsm.2017.09.035.

摘要

基于黄登引水发电系统区域地应力实测结果及建立的三维数值仿真计算模型，揭示了工程所在区域的三维地应力场分布特征，为地下工程的开挖加固设计提供更加准确的基础资料。分别采用传统多元线性回归方法、人工神经网络方法与考虑地质历史过程的基于逐步回归原理耦合人工神经网络（SLR-ANN）的非线性智能方法获得黄登水电站厂址区域的地应力场，再将地应力的实测值与反演数值解进行对比。结果表明：3种方法下反演所得引水发电系统区域内三维地应力场均与实测结果相一致，表明3种方法较为真实地模拟了整个地下洞室群区域三维地应力场的分布规律及特征。但采用SLR-ANN二次智能反演方法进行地应力反演，模拟效果更加接近监测值，且因减少了反演参数的个数而大幅度地提高了反演效率，可将反演计算结果应用于后续洞室开挖及锚固仿真分析中。

Abstract

Based on in-situ stress measurement and three-dimensional (3D) inversion computing model of Huangdeng large-scale underground cavern

this study is to revearl the distribution characteristics of in-situ stress fields and further to provide accurately initial material for the design of the underground excavation and reinforcement. The inversions of in-situ stress fields are obtained by the multiple linear regression (MLR)

the artificial neural network (ANN) and the stepwise linear regression (SLR) combined with ANN

respectively. Moreover

the SLR-ANN method is characterized by the nonlinear intelligent inversion analysis

which especially considers the geological history process. In addition

the results of inversion analysis are proofread and examined with measured in-situ stress results. It shows that the inversion results by these three methods are in good agreement with measured results. The results indicate that these three methods can truly reflect the distributions and characteristics of 3D in-situ stress in the whole underground cavern group. Compared with these three methods

the results by SLR-ANN algorithm are closest to measured results. This method also significantly improves the inversion efficiency by reducing inversion parameters. Therefore

it can be practically applied in realistic scenarios to achieve efficient and accurate estimations of in-situ stress in rock mass.

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