[1]邓志平,牛景太,潘敏,等.基于不同材料分类模型的地层变异性模拟比较研究[J].自然灾害学报,2018,(04):128-136.[doi:10.13577/j.jnd.2018.0417]
 DENG Zhiping,NIU Jingtai,PAN Min,et al.Comparative study on geological uncertainty simulation based on different material classification models[J].,2018,(04):128-136.[doi:10.13577/j.jnd.2018.0417]
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基于不同材料分类模型的地层变异性模拟比较研究
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《自然灾害学报》[ISSN:/CN:23-1324/X]

卷:
期数:
2018年04期
页码:
128-136
栏目:
出版日期:
2018-09-28

文章信息/Info

Title:
Comparative study on geological uncertainty simulation based on different material classification models
作者:
邓志平12 牛景太12 潘敏12 张阳12 郭英嘉12
1. 南昌工程学院 水利与生态工程学院, 江西 南昌 330099;
2. 南昌工程学院 鄱阳湖流域水工程安全与资源高效利用国家地方联合工程实验室, 江西 南昌 330099
Author(s):
DENG Zhiping12 NIU Jingtai12 PAN Min12 ZHANG Yang12 GUO Yingjia12
1. School of Water Resources and Ecological Engineering, Nanchang Institute of Technology, Nanchang 330099, China;
2. National Provincial Joint Engineering Laboratory for the Hydraulic Engineering Safety and Efficient Utilization of Water Resources of Poyang Lake Basin, Nanchang 330099, China
关键词:
地层变异性耦合马尔可夫链广义耦合马尔可夫链水平转移概率矩阵信息熵
Keywords:
geological uncertaintycoupled Markov chaingeneralized coupled Markov chainhorizontal transition probabilityinformation entropy
分类号:
TU43;X43;X9
DOI:
10.13577/j.jnd.2018.0417
摘要:
岩土结构物变形与稳定性评估的准确性往往取决于地层描述的精确程度。实际地层往往表现出变异性,材料分类模型能够对地层变异性进行有效地描述,目前常用的材料分类模型有主要有耦合马尔可夫链和广义耦合马尔可夫链,目前的研究中并未对两种模型进行系统的比较,给工程师们在模型选择方面带来困难。为此,以香港地区某场地地层为例,对两种材料分类模型的转移概率矩阵、信息熵图、地层变异模拟结果等方面进行系统的比较。结果表明:广义耦合马尔可夫链能够有效利用不同方向上的转移概率矩阵进行地层变异模拟,而耦合马尔可夫链在竖直方向或水平方向仅利用了一个方向上的转移概率矩阵。广义耦合马尔可夫链模型模拟所对应的不确定性范围要大于耦合马尔可夫链模型模拟的结果。广义耦合马尔可夫链模拟所得的地层分布相比耦合马尔可夫链模型所得的更合理。
Abstract:
The accuracy of the deformation and stability evaluation of geotechnical structures often depends upon a precise description of the subsurface stratigraphy. Actual strata often show variability, which can be described by material classification models. The commonly used material classification models are mainly coupled Markov chain and generalized coupled Markov chain. The current study does not systematically compare the two models, giving engineers the difficulties in model selection. Hence, taking a site formation in Hong Kong as an example, a systematic comparison of the transfer probability matrix, information entropy map, and geological uncertainty simulation results of the two material classification models was conducted. The results show that the generalized coupled Markov chain can effectively use the transition probability matrix in different directions to simulate the geological uncertainty, while the coupled Markov chain only uses the transition probability matrix in one direction in the vertical or horizontal direction. The uncertainty range corresponding to the simulation of the generalized coupled Markov chain model is greater than that of the coupled Markov chain model. The strata distribution obtained by the simulation of the generalized coupled Markov chain is more reasonable than the coupled Markov chain model.

参考文献/References:

[1] 祁小辉, 李典庆, 曹子君, 等. 考虑地层变异的边坡稳定不确定性分析[J]. 岩土力学, 2017, 38(5):1385-1396. QI Xiaohui, LI Dianqing, CAO Zijun, et al. Uncertainty analysis of slope stability considering geologic uncertainty[J]. Rock and Soil Mechanics, 2017, 38(5):1385-1396. (in Chinese)
[2] Hu Q F, Huang H W. Risk analysis of soil transition in tunnel works[C]//Proc., 33rd ITA-AITES World Tunnel Congress-Underground Space-The 4th Dimension of Metropolises, Taylor & Francis, London, 209-215.
[3] Wang X, Wang H, Liang R Y. A method for slope stability analysis considering subsurface stratigraphic uncertainty[J]. Landslides, 2017(8):1-12.
[4] 邓志平, 李典庆, 曹子君, 等. 考虑地层变异性和土体参数变异性的边坡可靠度分析[J]. 岩土工程学报, 2017, 39(6):986-995. DENG Zhiping, LI Dianqing, CAO Zijun, et al. Slope reliability analysis considering geological uncertainty and spatial variability of soil parameters[J]. Chinese Journal of Geotechnical Engineering, 2017, 39(6):986-995. (in Chinese)
[5] 祁小辉, 李典庆, 周创兵, 等. 基于钻孔资料的耦合马尔可夫链水平方向转移概率矩阵估计[J]. 应用基础与工程科学学报, 2017, 25(5):967-984. QI Xiaohui, LI Dianqing, ZHOU Chuangbing, et al. Estimation of horizontal transition probability matrix for coupled Markov chain based on borehole data[J]. Journal of Basic Science and Engineering, 2017, 25(5):967-984. (in Chinese)
[6] Liao T, Mayne P W. Stratigraphic delineation by three-dimensional clustering of piezocone data[J]. Georisk Assessment & Management of Risk for Engineered Systems & Geohazards, 2007, 1(2):102-119.
[7] Cao Z, Wang Y. Bayesian approach for probabilistic site characterization using Cone penetration tests[J]. Journal of Geotechnical and Geoenvironmental Engineering, 2013, 139(2):267-276.
[8] Ching J, Wang J, Juang C H, et al. Cone penetration test (CPT)-based stratigraphic profiling using the wavelet transform modulus maxima method[J]. Canadian Geotechnical Journal, 2015, 52(12):1993-2007.
[9] 曹子君, 郑硕, 李典庆, 等. 基于静力触探的土层自动划分方法与不确定性表征[J]. 岩土工程学报, 2018, 40(2):336-345. CAO Zijun, ZHENG Shuo, LI Dianqing, et al. Probabilistic characterization of underground stratigraphy and its uncertainty based on cone penetration test[J]. Chinese Journal of Geotechnical Engineering, 2018, 40(2):336-345. (in Chinese)
[10] Nobre M M, Sykes J F. Application of Bayesian Kriging to subsurface characterization[J]. Canadian Geotechnical Journal, 2011, 29(4):589-598.
[11] Dasaka S M, Zhang L M. Spatial variability of in situ weathered soil[J]. Géotechnique, 2012, 62(5):375-384.
[12] Schöbi R, Sudret B. Application of conditional random fields and sparse polynomial chaos expansions to geotechnical problems[C]//Proc., 5th Int. Symp. on Geotechnical Safety and Risk, T. Schweckendiek, et al. eds, IOS Press, Rotterdam, Netherlands, 2015, 441-446.
[13] Zhang L M, Dasaka S M. Uncertainties in geologic profiles vs. variability in pile founding depth[J]. Journal of Geotechnical & Geoenvironmental Engineering, 2010, 136(11):1475-1488.
[14] Sitharam T G, Samui P, Anbazhagan P. Spatial variability of rock depth in bangalore using geostatistical, neural network and support vector machine models[J]. Geotechnical & Geological Engineering, 2008, 26(5):503-517.
[15] LI X Y, Zhang L M, Li J H. Using conditioned random field to characterize the variability of geologic profiles[J]. Journal of Geotechnical and Geoenvironmental Engineering, 2015, 142(4):04015096.
[16] Xiao T, Zhang L M, Li X Y, et al. Probabilistic stratification modeling in geotechnical site characterization[J]. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A:Civil Engineering, 2017, 3(4):04017019.
[17] Elfeki A, Dekking M. A Markov chain model for subsurface characterization:theory and applications[J]. Mathematical Geology, 2001, 33(5):569-589.
[18] Park E, Elfeki A, Song Y, et al. Generalized coupled markov chain model for characterizing categorical variables in soil mapping[J]. 2007, 71(3):909-917.
[19] Park E. A multidimensional, generalized coupled Markov chain model for surface and subsurface characterization[J]. Water Resources Research, 2010, 46(11):6291-6297.
[20] 邓志平, 李典庆, 祁小辉, 等. 基于广义耦合马尔可夫链的地层变异性模拟方法[J]. 岩土工程学报, 2018. DENG Zhiping, LI Dianqing, QI Xiaohui, et al. Simulation of geological uncertainty using modified generalized coupled Markov chain[J]. Chinese Journal of Geotechnical Engineering, 2018. (in Chinese)
[21] Wellmann J F, Regenauer-lieb K. Uncertainties have a meaning:Information entropy as a quality measure for 3-D geological models[J]. Tectonophysics, 2012, 526-529(2):207-216.
[22] LI Z, WANG X, WANG H, et al. Quantifying stratigraphic uncertainties by stochastic simulation techniques based on Markov random field[J]. Engineering Geology, 2016:106-122.
[23] LI W, ZHANG C. A single-chain-based multidimensional Markov chain model for subsurface characterization[J]. Environmental and Ecological Statistics, 2008, 15(2):157-174.

备注/Memo

备注/Memo:
收稿日期:2018-05-01;改回日期:2018-05-18。
基金项目:国家自然科学基金项目(51769017)
作者简介:邓志平(1990-),男,讲师,博士,主要从事岩土工程可靠度分析与风险控制方面的研究.E-mail:zhipingdeng10@126.com
通讯作者:牛景太(1977-),男,副教授,博士,主要从事水工建筑物及高边坡安全监控研究.E-mail:niujingtai@163.com
更新日期/Last Update: 1900-01-01