CPTU数据校准上海深层软土参数的随机力学-贝叶斯方法

王长虹; 吴昭欣; 王昆; 汤道飞; 马铖涛

doi:10.11779/CJGE20211494

CPTU数据校准上海深层软土参数的随机力学-贝叶斯方法 English Version

上海大学力学与工程科学学院土木工程系, 上海 200444

基金项目:

上海高校特聘教授岗位计划项目 TP2018042

上海市浦江人才计划项目 18PJ1403900

上海市社会发展科技攻关项目 21DZ1204300

详细信息

作者简介:
王长虹(1978—)，上海高校特聘教授，主要从事随机岩土力学-贝叶斯方法研究。E-mail: changhong-wang@163.com

通讯作者:
王昆, E-mail: wang-wkun@shu.edu.cn

中图分类号: TU413
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出版历程
- 收稿日期: 2021-12-09
- 网络出版日期: 2023-02-03
- 发布日期: 2021-12-09
- 刊出日期: 2022-12-31

Stochastic mechanics-based Bayesian method for calibrating geotechnical parameters of Shanghai deep soft clay using CPTU data

Department of Civil Engineering, School of Mechanics and Engineering Sciences, Shanghai University, Shanghai 200444, China

摘要

摘要: 城市深层地下空间的开发需要开展科学计算。合理的本构模型和准确的岩土参数是岩土力学分析的两大支撑。修正剑桥模型（modified cam-clay, MCC）的岩土参数简单直观，被广泛应用于岩土工程实践。室内土工试验由于取样扰动、试验误差等不可避免的缺陷，难以精确获得深层岩土参数。结合先验知识、室内试验数据和孔压静力触探（piezocone penetration test, CPTU）测试数据，将关键岩土参数视为随机变量，采用随机力学-贝叶斯方法，校准深层软土的关键岩土参数，如临界状态应力比、压缩系数、回弹系数和超固结比。以上海市苏州河深层排水调蓄管道系统工程——云岭竖井基坑工程为背景，以基础底板处的深部第⑧层土为研究对象。首先，利用MCC模型的柱孔扩张理论，揭示了CPTU数据（锥尖阻力、侧摩阻力和孔隙水压力）与极限扩孔应力之间的力学转换机理，利用苏格兰Bothkennar岩土试验场地的试验数据，验证CPTU测试数据力学模型的适用性；其次，建立关键岩土参数与CPTU数据之间的二次非交叉响应面，利用MCMC（Markov-chain Monte Carlo）抽样算法，获得关键岩土参数的后验统计特征；最后，结合岩土参数的后验均值，开展超深基坑工程的数值计算。结果表明，经过柱孔扩张随机力学-贝叶斯方法校准后，关键岩土参数的不确定性大幅度降低，后验均值的预测结果与监测值较为接近，证明了该方法的高效、合理性。
- 孔压静力触探 /
- 柱孔扩张理论 /
- 随机力学 /
- 岩土参数 /
- 超深基坑
Abstract: The construction of urban deep underground engineering requires scientific calculation. The reasonable constitutive model and accurate geotechnical parameters are the most important support for soil mechanics. The modified Cam-clay (MCC) model is a simple one as its constants are simple and intuitive, and is widely used in geotechnical engineering. However, it's different to get precise parameters in deep soft clay by the indoor tests due to sampling disturbance, test error and other unavoidable factors. Considering the prior information, laboratory test data, and piezocone penetration test (CPTU) data, the key geotechnical parameters are treated as random variables, the stochastic mechanics-Bayesian method is proposed to calibrate the key geotechnical parameters of the deep soil layers, such as the critical state stress ratio, compression coefficient, rebound coefficient, and overconsolidation ratio. Based on the Suzhou River deep drainage and storage pipeline system in Shanghai and the foundation pit of Yunling, the deep soil layer ⑧ at the base plate is taken as the research object. Firstly, the mechanical conversion between the CPTU data (i.e., cone tip resistance, lateral friction stress and pore pressure) and the limit expansion pressure of cylindrical cavity is derived in the MCC model. The mechanical conversion for the CPTU data is verified by using the test data from Bothkennar geotechnical test site in Scotland. Secondly, the quadratic response surface without being crossed between the key geotechnical parameters and the CPTU data can be established by regression. The Markov-chain Monte Carlo (MCMC) sampling method will obtain the posterior distributions of the key geotechnical parameters. Finally, The numercial calculation for the foundation pit is carried out with the mean values of the posterior geotechnical parameters. The results show that the uncertainties of the geotechnical parameters are significantly reduced, and the results obtained by the numerical simulation of the foundation pit using the mean values of geotechnical parameters are closer to the monitoring values. It is proved that the stochastic mechanics- based Bayesian method is effective and efficient.
- piezocone penetration test /
- cylindrical cavity expansion theory /
- stochastic mechanics /
- geotechnical parameter /
- extremely deep excavation

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图 1 CPTU贯入计算模型

Figure 1. Principle of CPTU model

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图 2 Bothkennar地区CPTU测试数据实测值与预测值

Figure 2. Predicted and measured CPTU profiles at Bothkennar site in Scotland

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图 3 随机力学-贝叶斯理论计算框架

Figure 3. Stochastic mechanics-based Bayesian theoretical framework

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图 4 苏州河深层排水调蓄管道系统及云岭综合设施工程概况

Figure 4. Schematic diagram of surrounding environment of Yunling complex

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图 5 岩土参数的敏感性分析结果

Figure 5. Results of sensitivity analysis for geotechnical parameters

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图 6 第⑧₁层粉质黏土关键岩土参数的先验和后验分布

Figure 6. Prior and posterior distributions of key geotechnical parameters in silty clay layer ⑧₁

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图 7 第⑧₁层土CPTU测试数据的预测包络图

Figure 7. Predicted CPTU inversion envelopes of silty clay layer ⑧₁

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图 8 第⑧₂层夹砂粉质黏土关键岩土参数的先验和后验分布

Figure 8. Prior and posterior distribution of key constitutive parameters of sandy silty clay layer ⑧₂

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图 9 第⑧₂层土CPTU测试数据的预测包络图

Figure 9. Predicted CPTU inversion envelopes of sandy silty ⑧₂

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图 10 数值模型概况与有限元网格划分

Figure 10. Overview of numerical model and finite element mesh

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图 11 基坑监测布置图

Figure 11. Monitoring points for foundation pit

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图 12 土体侧向位移图

Figure 12. Lateral displacements of soil of diaphragm wall

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图 13 基坑中心处土体隆起值变化

Figure 13. Uplift values of soil at center of foundation pit

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表 1 第⑧层土主要岩土参数的先验数据

Table 1 Key geotechnical parameters of silty clay layer ⑧

土层	临界状态应力比 $M$	压缩系数 $\lambda$	回弹系数 $\kappa$	超固结比 ${\text{OCR}}$	孔隙比 $e$	侧压力系数 ${K_0}$	泊松比 $\nu$
⑧₁粉质黏土	1.14~1.36	0.10~0.18	0.008~0.022	1.01~2.40	0.90~1.10	0.47~0.49	0.31~0.33
⑧₂夹砂粉质黏土	1.28~1.36	0.10~0.15	0.008~0.022	1.20~2.61	0.62~1.01	0.43~0.49	0.31~0.32

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参考文献(25)

[1]	DUAN W, CHANDRA C S S, CAI G J, et al. Empirical correlations of soil parameters based on piezocone penetration tests (CPTU) for Hong Kong-Zhuhai-Macau Bridge (HZMB) project[J]. Transportation Geotechnics, 2021, 30: 100605. doi: 10.1016/j.trgeo.2021.100605
[2]	LIU X Y, CAI G J, LIU L L, et al. Improved p-y curve models for large diameter and super-long cast-in-place piles using piezocone penetration test data[J]. Computers and Geotechnics, 2021, 130: 103911. doi: 10.1016/j.compgeo.2020.103911
[3]	SEDMAK V A. Expansion of cavities in infinite soil mass[J]. Journal of the Soil Mechanics and Foundations Division, 1972, 98(3): 265-290. doi: 10.1061/JSFEAQ.0001740
[4]	李镜培, 李林, 孙德安, 等. 饱和软土地层静压沉桩阻力理论研究[J]. 岩土工程学报, 2015, 37(8): 1454-1461. doi: 10.11779/CJGE201508014 LI Jingpei, LI Lin, SUN De'an, et al. Theoretical study on sinking resistance of jacked piles in saturated soft clay[J]. Chinese Journal of Geotechnical Engineering, 2015, 37(8): 1454-1461. (in Chinese) doi: 10.11779/CJGE201508014
[5]	郑金辉, 齐昌广, 王新泉, 等. 考虑砂土颗粒破碎的柱孔扩张问题弹塑性分析[J]. 岩土工程学报, 2019, 41(11): 2156-2164. doi: 10.11779/CJGE201911023 ZHENG Jinhui, QI Changguang, WANG Xinquan, et al. Elasto-plastic analysis of cylindrical cavity expansion considering particle breakage of sand[J]. Chinese Journal of Geotechnical Engineering, 2019, 41(11): 2156-2164. (in Chinese) doi: 10.11779/CJGE201911023
[6]	武孝天. 搅拌桩和管桩施工的挤土效应及其控制措施研究[D]. 上海: 上海交通大学, 2020. WU Xiaotian. Study on the Squeezing Effect and its Control Measures for Mixing Piles and Pipe Piles Construction[D]. Shanghai: Shanghai Jiao Tong University, 2020. (in Chinese)
[7]	ROBERTSON P K. Cone penetration test (CPT)-based soil behaviour type (SBT) classification system—an update[J]. Canadian Geotechnical Journal, 2016, 53(12): 1910-1927. doi: 10.1139/cgj-2016-0044
[8]	蔡国军, 刘松玉, 童立元, 等. 基于静力触探测试的国内外砂土液化判别方法[J]. 岩石力学与工程学报, 2008, 27(5): 1019-1027. https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX200805021.htm CAI Guojun, LIU Songyu, TONG Liyuan, et al. Evaluation of liquefaction of sandy soils based on cone penetration test[J]. Chinese Journal of Rock Mechanics and Engineering, 2008, 27(5): 1019-1027. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX200805021.htm
[9]	刘松玉, 郭易木, 张国柱, 等. 热传导CPT探头的研发与应用[J]. 岩土工程学报, 2020, 42(2): 354-361. doi: 10.11779/CJGE202002017 LIU Songyu, GUO Yimu, ZHANG Guozhu, et al. Development and application of heat conduction CPT probe[J]. Chinese Journal of Geotechnical Engineering, 2020, 42(2): 354-361. (in Chinese) doi: 10.11779/CJGE202002017
[10]	蒋水华, 冯泽文, 刘贤, 等. 基于自适应贝叶斯更新方法的岩土参数概率分布推断[J]. 岩土力学, 2020, 41(1): 325-335. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX202001038.htm JIANG Shuihua, FENG Zewen, LIU Xian, et al. Inference of probability distributions of geotechnical parameters using adaptive Bayesian updating approach[J]. Rock and Soil Mechanics, 2020, 41(1): 325-335. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX202001038.htm
[11]	郑栋, 黄劲松, 李典庆. 基于多源信息融合的路堤沉降预测方法[J]. 岩土力学, 2019, 40(2): 709-719, 727. https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201902034.htm ZHENG Dong, HUANG Jinsong, LI Dianqing. An approach for predicting embankment settlement by integrating multi-source information[J]. Rock and Soil Mechanics, 2019, 40(2): 709-719, 727. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YTLX201902034.htm
[12]	WANG C H, OSORIO-MURILLO C A, ZHU H H, et al. Bayesian approach for calibrating transformation model from spatially varied CPT data to regular geotechnical parameter[J]. Computers and Geotechnics, 2017, 85: 262-273.
[13]	CHING J, PHOON K. Constructing site-specific multivariate probability distribution model by Bayesian machine learning[J]. ASCE Journal of Engineering Mechanics, 2019, 145(01): 04018126.
[14]	ROSCOE K H, BURLAND J B. On the Generalized Stress-strain Behavior of "Wet" Clay[C]// In Engineering plasticity. Cambridge: Cambridge University Press, 1968, 535-609.
[15]	CHEN S L, ABOUSLEIMAN Y. Exact undrained elasto-plastic solution for cylindrical cavity expansion in modified Cam Clay soil[J]. Geotechnique, 2012, 62(5): 447-456.
[16]	CHEN S L, ABOUSLEIMAN Y N. Exact drained solution for cylindrical cavity expansion in modified Cam-clay soil[J]. Géotechnique, 2013, 63(6): 510-517.
[17]	MO P Q, GAO X W, YANG W B, et al. A cavity expansion-based solution for interpretation of CPTu data in soils under partially drained conditions[J]. International Journal for Numerical and Analytical Methods in Geomechanics, 2020, 44(7): 1053-1076.
[18]	孔压静力触探技术规程: DB32/T 2977—2016[S]. 南京: 江苏省质量技术监督局, 2016. Technical Specification for Piezocone Penetration Test: DB32/T 2977—2016[S]. Nanjing: Jiangsu Provincial Bureau of Quality and Technical Supervision, 2016. (in Chinese)
[19]	HIGHT D W, BOND A J, LEGGE J D. Characterization of the Bothkennaar clay: an overview[J]. Géotechnique, 1992, 42(2): 303-347.
[20]	韦来生. 贝叶斯统计[M]. 北京: 高等教育出版社, 2016. WEI Laisheng. Bayesian Statistics[M]. Beijing: Higher Education Press, 2016. (in Chinese)
[21]	RUBIN Y, CHEN X Y, MURAKAMI H, et al. A Bayesian approach for inverse modeling, data assimilation, and conditional simulation of spatial random fields[J]. Water Resources Research, 2010, 46(10): 2009WR008799.
[22]	武朝军. 上海浅部土层沉积环境及其物理力学性质[D]. 上海: 上海交通大学, 2016. WU Zhaojun. Depositional Environment and Geotechnical Properties for the Upper Shanghai Clays[D]. Shanghai: Shanghai Jiao Tong University, 2016. (in Chinese)
[23]	刘丽斌. 上海深层黏土的物理性质、超固结特性及本构模拟[D]. 上海: 上海交通大学, 2019. LIU Libin. Physical Properties, Over-Consolidation and Constitutive Modeling of Shanghai Deep Clays[D]. Shanghai: Shanghai Jiao Tong University, 2019. (in Chinese)
[24]	何为, 薛卫东, 唐斌. 优化试验设计方法及数据分析[M]. 北京: 化学工业出版社, 2012. HE Wei, XUE Weidong, TANG Bin. Optimization Design Method and Data Analysis[M]. Beijing: Chemical Industry Press, 2012. (in Chinese)
[25]	XU Z H, LI J, WENG Q P, et al. Analysis method of ultra-deep circular excavation and its application[J]. Construction Technology, 2022, 51(1): 13-20.