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

WANG Changhong; WU Zhaoxin; WANG Kun; TANG Daofei; MA Chengtao

doi:10.11779/CJGE20211494

Volume 45 Issue 1

Turn off MathJax

Article Contents

Abstract

References

Supplements

Chinese Journal of Geotechnical Engineering > 2023 > 45(1): 75-84. > DOI: 10.11779/CJGE20211494

WANG Changhong, WU Zhaoxin, WANG Kun, TANG Daofei, MA Chengtao. Stochastic mechanics-based Bayesian method for calibrating geotechnical parameters of Shanghai deep soft clay using CPTU data[J]. Chinese Journal of Geotechnical Engineering, 2023, 45(1): 75-84. DOI: 10.11779/CJGE20211494

Citation:

PDF (4875 KB)

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

More Information

Received Date: December 09, 2021
Available Online: February 03, 2023
Published Date: December 09, 2021

Graphical Abstract

Abstract

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.

FullText(HTML)

References (25)

References

[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.