勘察数据变化条件下空间变异土坡稳定可靠度协同更新方法

田华明; 李典庆

doi:10.11779/CJGE20221579

勘察数据变化条件下空间变异土坡稳定可靠度协同更新方法 English Version

田华明^{1, 2},
李典庆^{1, 2, ,}

1.
武汉大学水资源工程与调度全国重点实验室, 湖北武汉 430072
2.
武汉大学水利水电学院工程风险与防灾研究所, 湖北武汉 430072

基金项目:

国家自然科学基金重点项目 U2240211

详细信息

作者简介:
田华明(1993—)，男，博士，主要从事岩土工程可靠度与风险控制方面的研究工作。E-mail: huamingtian@ whu.edu.cn

通讯作者:
李典庆, E-mail: dianqing@whu.edu.cn

中图分类号: TU431
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出版历程
- 收稿日期: 2022-12-27
- 网络出版日期: 2024-03-24
- 刊出日期: 2024-07-31

Collaborative reliability updating of slopes with spatially varying soil properties considering different site investigation data

TIAN Huaming^{1, 2},
LI Dianqing^{1, 2, ,}

1.
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China
2.
Institute of Engineering Risk and Disaster Prevention, School of Water Resources and Hydropower Engineering, Wuhan University, Wuhan 430072, China

摘要

摘要: 贝叶斯理论为合理表征土性参数的空间变异性以及量化勘察数据（如不排水剪切强度值）对边坡稳定可靠度的影响提供了有效工具。然而，勘察数据随边坡空间位置变化，而且土性参数空间变异性表征模型（如随机场模型）包含高维不确定性参数。勘察数据的动态变化与高维随机参数反演导致基于贝叶斯理论的边坡稳定可靠度更新具有显著的计算量。为此，提出了考虑变化勘察数据的空间变异土坡稳定可靠度协同更新方法。该方法首先基于贝叶斯更新框架BUS模拟条件随机场，执行边坡稳定性分析；然后基于拒绝抽样原理和协同式分析方法根据不同勘察数据表征岩土体参数的空间变异性，并更新边坡的稳定可靠度。当勘察数据在边坡不同空间位置变化时，避免了重新模拟条件随机场以及执行边坡稳定性分析，同时继承了BUS解决高维随机参数反演的特性，实现了基于贝叶斯理论的空间变异边坡稳定可靠度快速更新。以单层非平稳随机场土坡为例验证了所提方法的合理性和有效性。结果表明，所提方法为勘察数据变化条件下土性参数空间变异性动态表征以及边坡稳定可靠度实时更新提供重要工具。
- 土坡 /
- 空间变异性 /
- 勘察数据 /
- 贝叶斯更新 /
- 协同式分析 /
- 稳定可靠度
Abstract: The Bayesian theory provides an effective tool to properly characterize the spatial variability of soil properties and quantify the effect of site investigation data (e.g., undrained shear strength data) on reliability of slope stability. However, the site investigation data sequentially appears at different spatial locations of a slope, and the model to characterize the spatially varying soil properties (e.g., random field model) usually involves a great number of uncertain parameters. These pose a great computational challenge for Bayesian updating of slope reliability considering spatially varying soil properties. A collaborative reliability updating approach for the slope stability with spatially varying soil properties considering different site investigation data is proposed. It first makes use of the Bayesian updating with structural reliability methods (BUS) to simulate random fields and perform slope stability analyses, and then employs the rejection sampling principle and collaborative analysis to characterize the spatially varying soil properties and update the reliability of slope stability considering different test data. As the site investigation data spatially appears within a slope, repeated simulations of conditional random fields and a significant number of slope stability analyses are avoided. Moreover, the combination of the BUS makes it possible for efficient slope reliability updating using the Bayesian analysis that involves high-dimensional model parameters. A single-layered soil slope with a non-stationary random field is employed to demonstrate the effectiveness and validity of the proposed approach. It is shown that the proposed approach provides an effective tool for dynamic characterization of spatial variability of soils and real-time reliability updating of slope stability under different site investigation data.
- soil slope /
- spatial variability /
- site investigation data /
- Bayesian updating /
- collaborative analysis /
- reliability assessment

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图 1 空间变异土坡稳定可靠度协同示意图

Figure 1. Schematic diagram of collaborative reliability updating of slope stability with spatially varying soil properties

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图 2 空间变异土坡稳定可靠度协同更新计算流程

Figure 2. Implementation flowchart of collaborative reliability updating of slope stability with spatially varying soil properties

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图 3 边坡稳定性分析模型与勘察数据

Figure 3. Slope stability model and site investigation data

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图 4 考虑x = 22.5 m钻孔勘察数据的条件随机场表征及边坡稳定可靠度更新

Figure 4. Modeling of conditional random field and updating of slope reliability with site investigation data at x = 22.5 m

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图 5 考虑不同钻孔位置勘察数据的条件随机场表征

Figure 5. Modeling of conditional random field considering site investigation data at different borehole locations

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图 6 考虑不同钻孔位置勘察数据的边坡稳定可靠度更新

Figure 6. Reliability updating of slope stability considering different site investigation data at various borehole locations

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表 1 非平稳随机场模型参数的先验统计量

Table 1 Prior statistics of random parameters for non-stationary random field

参数	分布类型	统计特征
S_u0	对数正态分布	${\mu _{{S_{{\text{u}}0}}}}$ = 14.67 kPa; ${\sigma _{{S_{{\text{u}}0}}}}$ = 4.04 kPa
b	对数正态分布	${\mu _{\text{b}}}$ = 0.272; ${\sigma _{\text{b}}}$ = 0.189
w(q)	高斯随机场	${\mu _{\text{w}}}$ = 0.272; ${\sigma _{\text{w}}}$ = 0.189 ${\lambda _{\text{h}}}$ = 38 m; ${\lambda _{\text{v}}}$ = 3.8 m

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表 2 考虑不同勘察数据的土性参数条件随机场表征与边坡稳定可靠度更新计算时间单位: s

Table 2 Computational time for characterization of spatial variability of soils considering different site investigation data

计算时间	图 4 (a)	图 4 (b)	图 5 (a)	图 5 (b)	图 5 (c)
本文方法	20.4	34.8	6.6	6.5	6.6
RCBU	3640.2	14411.4	1860.4	1878.6	1473.7
计算时间	图 5 (d)	图 5 (e)	图 5 (f)	图 6 (a)	图 6 (b)
本文方法	6.5	6.5	6.0	21.2	21.0
RCBU	1238.9	1485.6	1235.8	15016.3	14323.2
计算时间	图 6 (c)	图 6 (d)	图 6 (e)	图 6 (f)
本文方法	16.0	13.2	12.9	8.2
RCBU	11324.7	8308.1	8855.3	6231.1
注：所提方法驱动贝叶斯分析计算时间为6480.4 s；记录时间的计算机配置为AMD Ryzen 7 5800H with Radeon Graphics 3.20 GHz。

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参考文献(21)

[1]	GRIFFITHS D V, FENTON G A. Probabilistic Slope Stability Analysis by Finite Elements[J]. Journal of Geotechnical and Geoenvironmental Engineering, 2004, 130(5): 507-518. doi: 10.1061/(ASCE)1090-0241(2004)130:5(507)
[2]	陈朝晖, 黄凯华. 土质边坡可靠性分析的分层非平稳随机场模型[J]. 岩土工程学报, 2020, 42(7): 1247-1256. doi: 10.11779/CJGE202007008 CHEN Zhaohui, HUANG Kaihua. Non-homogeneous random field model for reliability analysis of slopes[J]. Chinese Journal of Geotechnical Engineering, 2020, 42(7): 1247-1256. (in Chinese) doi: 10.11779/CJGE202007008
[3]	LI D, XIAO T, CAO Z, et al. Efficient and consistent reliability analysis of soil slope stability using both limit equilibrium analysis and finite element analysis[J]. Applied Mathematical Modelling, 2016, 40(9/10): 5216-5229.
[4]	邓志平, 牛景太, 潘敏, 等. 考虑地层变异性和土体参数空间变异性的边坡可靠度全概率设计方法[J]. 岩土工程学报, 2019, 41(6): 1083-1090. doi: 10.11779/CJGE201906012 DENG Zhiping, NIU Jingtai, PAN Min, et al. Full probabilistic design method for slopes considering geological uncertainty and spatial variability of soil parameters[J]. Chinese Journal of Geotechnical Engineering, 2019, 41(6): 1083-1090. (in Chinese) doi: 10.11779/CJGE201906012
[5]	谭晓慧, 董小乐, 费锁柱, 等. 基于KL展开的可靠度分析方法及其应用[J]. 岩土工程学报, 2020, 42(5): 808-816. doi: 10.11779/CJGE202005002 TAN Xiaohui, DONG Xiaole, FEI Suozhu, et al. Reliability analysis method based on KL expansion and its application[J]. Chinese Journal of Geotechnical Engineering, 2020, 42(5): 808-816. (in Chinese) doi: 10.11779/CJGE202005002
[6]	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, 2016, 142(4): 04015096. doi: 10.1061/(ASCE)GT.1943-5606.0001428
[7]	LLORET-CABOT M, HICKS M A, VAN DEN EIJNDEN A P. Investigation of the reduction in uncertainty due to soil variability when conditioning a random field using Kriging[J]. Géotechnique Letters, 2012, 2(3): 123-127. doi: 10.1680/geolett.12.00022
[8]	ZHAO T, WANG Y. Determination of efficient sampling locations in geotechnical site characterization using information entropy and Bayesian compressive sampling[J]. Canadian Geotechnical Journal, 2019, 56(11): 1622-1637. doi: 10.1139/cgj-2018-0286
[9]	JIANG S, PAPAIOANNOU I, STRAUB D. Bayesian updating of slope reliability in spatially variable soils with in-situ measurements[J]. Engineering Geology, 2018, 239: 310-320. doi: 10.1016/j.enggeo.2018.03.021
[10]	JIANG S, PAPAIOANNOU I, STRAUB D. Optimization of site-exploration programs for slope-reliability assessment[J]. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 2020, 6(1): 4020004. doi: 10.1061/AJRUA6.0001042
[11]	STRAUB D, PAPAIOANNOU I, BETZ W. Bayesian analysis of rare events[J]. Journal of Computational Physics, 2016, 314: 538-556. doi: 10.1016/j.jcp.2016.03.018
[12]	蒋水华, 魏博文, 张文举, 等. 基于多源试验数据空间变异土体参数概率反演及边坡可靠度更新[J]. 岩土力学, 2018, 39(4): 1491-1499. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201804042.htm JIANG Shuihua, WEI Bowen, ZHANG Wenju, et al. Probabilistic back analysis of spatially varying soil properties and reliability updating of slopes with multiple sources of test data[J]. Rock and Soil Mechanics, 2018, 39(4): 1491-1499. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201804042.htm
[13]	STRAUB D, PAPAIOANNOU I. Bayesian Updating with Structural Reliability Methods[J]. Journal of Engineering Mechanics, 2015, 141(3): 04014134. doi: 10.1061/(ASCE)EM.1943-7889.0000839
[14]	蒋水华, 刘贤, 黄发明, 等. 考虑多参数空间变异性的降雨入渗边坡失稳机理及可靠度分析[J]. 岩土工程学报, 2020, 42(5): 900-907. doi: 10.11779/CJGE202005012 JIANG Shuihua, LIU Xian, HUANG Faming, et al. Failure mechanism and reliability analysis of soil slopes under rainfall infiltration considering spatial variability of multiple soil parameters[J]. Chinese Journal of Geotechnical Engineering, 2020, 42(5): 900-907. (in Chinese) doi: 10.11779/CJGE202005012
[15]	BETZ W, PAPAIOANNOU I, BECK J L, et al. Bayesian inference with Subset Simulation: Strategies and improvements[J]. Computer Methods in Applied Mechanics and Engineering, 2018, 331: 72-93. doi: 10.1016/j.cma.2017.11.021
[16]	AU S, BECK J L. Estimation of small failure probabilities in high dimensions by subset simulation[J]. Probabilistic Engineering Mechanics, 2001, 16(4): 263-277. doi: 10.1016/S0266-8920(01)00019-4
[17]	AU S K. Augmenting approximate solutions for consistent reliability analysis[J]. Probabilistic Engineering Mechanics, 2007, 22(1): 77-87. doi: 10.1016/j.probengmech.2006.08.004
[18]	AU S K, WANG Y. Engineering risk assessment with subset simulation[M]. Singapore: John Wiley and Sons, 2014.
[19]	TIAN H M, CAO Z J, LI D Q, et al. Efficient and flexible Bayesian updating of embankment settlement on soft soils based on different monitoring datasets[J]. Acta Geotechnica, 2022, 17(4): 1273-1294. doi: 10.1007/s11440-021-01378-4
[20]	TIAN H M, LI D Q, CAO Z J, et al. Auxiliary Bayesian updating of embankment settlement based on finite element model and response surface method[J]. Engineering Geology, 2023, 323: 107244. doi: 10.1016/j.enggeo.2023.107244
[21]	CHING J, PHOON K, CHEN Y. Reducing shear strength uncertainties in clays by multivariate correlations[J]. Canadian Geotechnical Journal, 2010, 47(1): 16-33. doi: 10.1139/T09-074