Full Text:   <960>

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CLC number: TU931

On-line Access: 2016-10-08

Received: 2016-01-11

Revision Accepted: 2016-02-19

Crosschecked: 2016-09-12

Cited: 0

Clicked: 2113

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

She-rong Zhang

http://orcid.org/0000-0002-7140-1878

Chao Wang

http://orcid.org/0000-0003-1853-2833

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Journal of Zhejiang University SCIENCE A 2016 Vol.17 No.10 P.782-802

http://doi.org/10.1631/jzus.A1600014


Three-dimensional inversion analysis of an in situ stress field based on a two-stage optimization algorithm


Author(s):  She-rong Zhang, An-kui Hu, Chao Wang

Affiliation(s):  State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China; more

Corresponding email(s):   wangchaosg@tju.edu.cn

Key Words:  In situ stress, Stepwise regression (SR), Difference evolution (DE), Support vector machine (SVM), Finite element, Huangdeng underground cavern


She-rong Zhang, An-kui Hu, Chao Wang. Three-dimensional inversion analysis of an in situ stress field based on a two-stage optimization algorithm[J]. Journal of Zhejiang University Science A, 2016, 17(10): 782-802.

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author="She-rong Zhang, An-kui Hu, Chao Wang",
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%A Chao Wang
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T1 - Three-dimensional inversion analysis of an in situ stress field based on a two-stage optimization algorithm
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DOI - 10.1631/jzus.A1600014


Abstract: 
Establishing an accurate In situ stress field is important for analyzing the rock-mass stability of the underground cavern at the Huangdeng hydropower station in China. Because of the complexity and importance of the In situ stress field, existing back analysis methods do not provide the necessary accuracy or sufficiently recognize nonlinear relations between the distribution of the In situ stress field and its formative factors. Those factors are related to the geological structures of high compressive tectonic stress regimes, including geological faults and tuff interlayers. The new two-stage optimization algorithm proposed in this paper is a combination of stepwise regression (SR), difference evolution (DE), support vector machine (SVM), and numerical analysis techniques. Stepwise regression is used to find the set of unknown parameters that best match the modeling prediction and determine the range of parameters to be recognized. Difference evolution is used to determine the optimum parameters of the SVM. The SVM is used to create the DE-SVM nonlinear reflection model to obtain the optimal values of the parameters from measured stress data. We compare the new two-stage optimization algorithm to other two popular methods, a multiple linear regression (MLR) analysis method and an artificial neural network (ANN) method, to estimate the In situ stress field for the actual underground cavern at the Huangdeng hydropower station. The two-stage optimization algorithm produces a more realistic estimate of the stress distribution within the investigated area. Thus, this technique may have practical applications in realistic scenarios requiring efficient and accurate estimations of the In situ stress in a rock-mass.

大型地下洞室群区域三维地应力场二次反演分析

目的:地下洞室群区域地应力分布繁杂多变,勘测点因数量有限难以反映初始地应力场空间分布特征。考虑工程区域内的地质构造、地形地貌及河谷的发育演化史等因素,提出综合反映工程区复杂地质条件及地层剥蚀过程的地应力场二次反演方法,揭示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。
创新点:1. 基于地应力场反演基本理论,建立逐步回归-差异进化-支持向量机模型(SR-DE-SVM)的二次反演非线性模型;2. 通过SR-DE-SVM算法计算流程,成功模拟工程区域地应力场分布。
方法:1. 通过工程勘测分析,推导出构造运动对工程区域地应力场分布产生的影响(表5和图13);2. 基于智能反演方法,构建SR-DE-SVM的二次反演非线性模型(公式(10)),得到SR-DE-SVM算法的计算流程(图2);3. 通过数值仿真模拟,结合地质历史的发展过程,验证所提出的二次反演方法的可行性和有效性(图10和11)。
结论:1. 工程区域内初始地应力水平属中等,主要受到岩体自重与构造运动的双重影响。2. SR-DE-SVM二次反演方法可更加清楚地明确初始地应力形成的主导成因,且更加准确、高效和真实地模拟工程区域三维地应力场的分布规律;3. 围岩类别对黄登地下洞室群区域内的初始地应力场影响不大,仅在断层带及III、IV类凝灰岩夹层带切割部位有显著的应力释放效应。

关键词:初始地应力;逐步回归;支持向量机;差异进化;有限元;黄登地下洞室

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Brown, E.T., Hoek, E., 1978. Trends in relationships between measured in-situ stresses and depth. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 15(4):211-215.

[2]Cheng, M.Y., Hoang, N.D., Wu, Y.W., 2013. Hybrid intelligence approach based on LS-SVM and differential evolution for construction cost index estimation: a Taiwan case study. Automation in Construction, 35:306-313.

[3]Feng, X.T., Zhang, Z., Sheng, Q., 2000. Estimating mechanical rock mass parameters relating to the three gorges project permanent shiplock using an intelligent displacement back analysis method. International Journal of Rock Mechanics and Mining Sciences, 37(7):1039-1054.

[4]Gong, M., Qi, S., Liu, J., 2010. Engineering geological problems related to high geo-stresses at the Jinping I Hydropower Station, Southwest China. Bulletin of Engineering Geology and the Environment, 69(3):373-380.

[5]González de Vallejo, L.I., Hijazo, T., 2008. A new method of estimating the ratio between in situ rock stresses and tectonics based on empirical and probabilistic analyses. Engineering Geology, 101(3-4):185-194.

[6]Grossberg, S., 1988. Nonlinear neural networks: principles, mechanisms, and architectures. Neural Networks, 1(1):17-61.

[7]Guo, H.Z., Ma, Q.C., Xue, X.C., et al., 1983. The analytical method of the initial stress field for rock masses. Chinese Journal of Geotechnical Engineering, 5(3):64-75 (in Chinese).

[8]Guo, M.W., Li, C.G., Wang, S.L., et al., 2008. Study on inverse analysis of 3-D initial geostress field with optimized displacement boundaries. Rock and Soil Mechanics, 29(5):1269-1274 (in Chinese).

[9]Hijazo, T., González de Vallejo, L.I., 2012. In-situ stress amplification due to geological factors in tunnels: the case of Pajares tunnels, Spain. Engineering Geology, 137-138:13-20.

[10]Jiang, A., Zhao, H., Jiang, S., 2013. Study on 3D intelligent back analysis of tunnel based on DE-SVM. Chinese Journal of Underground Space and Engineering, 9(4):765-770 (in Chinese).

[11]Jiang, Q., Feng, X.T., Chen, J.L., et al., 2008. Nonlinear inversion of 3D initial geostress field in Jinping II Hydropower Station region. Rock and Soil Mechanics, 29(11):3003-3010 (in Chinese).

[12]Kartam, N., Flood, I., Garrett, J.H., 1997. Artificial Neural Networks for Civil Engineers: Fundamentals and Applications. American Society of Civil Engineers, New York, USA.

[13]Li, Y.S., Yin, J.M., Chen, J.P., et al., 2012. Analysis of 3D in-situ stress field and query system’s development based on visual BP neural network. Procedia Earth and Planetary Science, 5:64-69.

[14]Liu, Y., Li, H., Luo, C., et al., 2014. In situ stress measurements by hydraulic fracturing in the western route of south to north water transfer project in China. Engineering Geology, 168:114-119.

[15]Mckinnon, S., 2001. Analysis of stress measurements using a numerical model methodology. International Journal of Rock Mechanics and Mining Sciences, 38(5):699-709.

[16]Qin, Z., Liu, C., Zhao, Z., et al., 2008. Back analysis of inintial ground stress by 3D-FSM considering influence of terrain and tectonic stress. Rock and Soil Mechanics, 7:027 (in Chinese).

[17]Saati, V., Mortazavi, A., 2011. Numerical modelling of in situ stress calculation using borehole slotter test. Tunnelling and Underground Space Technology, 26(1):172-178.

[18]Samui, P., Kim, D., Aiyer, B.G., 2015. Pullout capacity of small ground anchor: a least square support vector machine approach. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 16(4):295-301.

[19]Xing, B., Zhang, K., Sun, S., et al., 2015. Emotion-driven Chinese folk music-image retrieval based on DE-SVM. Neurocomputing, 148:619-627.

[20]Xu, R.Q., 2000. The GA-ANN method for determining calculation parameters for deep excavation. Journal of Zhejiang University-SCIENCE, 1(4):408-413.

[21]Xue, L., Chen, S., 2006. Two-stage analysis of geostress field for underground chamber area of Pubugou project. Chinese Journal of Rock Mechanics and Engineering, 25(9):1881-1886 (in Chinese).

[22]Zhang, L., Yue, Z., Yang, Z., et al., 2006. A displacement-based back-analysis method for rock mass modulus and horizontal in situ stress in tunneling–illustrated with a case study. Tunnelling and Underground Space Technology, 21(6):636-649.

[23]Zhang, S., Yin, S., 2014. Determination of in situ stresses and elastic parameters from hydraulic fracturing tests by geomechanics modeling and soft computing. Journal of Petroleum Science and Engineering, 124:484-492.

[24]Zhang, Z.M., 2011. Achievements and problems of geotechnical engineering investigation in China. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 12(2):87-102.

[25]Zhao, D., Chen, Z., Cai, X., et al., 2007. Analysis of distribution rule of geostress in China. Chinese Journal of Rock Mechanics and Engineering, 26(6):1265-1271 (in Chinese).

[26]Zhao, H., Ma, F., Xu, J., et al., 2012. In situ stress field inversion and its application in mining-induced rock mass movement. International Journal of Rock Mechanics and Mining Sciences, 53:120-128.

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