Efficient optimization identification method for soil stratification based on cone penetration test and joint posterior distribution of variable dimensionality

CAO Zijun; HU Chao; WANG Yafei; MIAO Cong; LIU Tao; HONG Yi; ZHENG Shuo

doi:10.11779/CJGE20230715

Volume 47 Issue 2

Turn off MathJax

Article Contents

Abstract

References

Supplements

Chinese Journal of Geotechnical Engineering > 2025 > 47(2): 346-354. > DOI: 10.11779/CJGE20230715

CAO Zijun, HU Chao, WANG Yafei, MIAO Cong, LIU Tao, HONG Yi, ZHENG Shuo. Efficient optimization identification method for soil stratification based on cone penetration test and joint posterior distribution of variable dimensionality[J]. Chinese Journal of Geotechnical Engineering, 2025, 47(2): 346-354. DOI: 10.11779/CJGE20230715

Citation:

PDF (2435 KB)

Efficient optimization identification method for soil stratification based on cone penetration test and joint posterior distribution of variable dimensionality

1.
MOE Key Laboratory of High-Speed Railway Engineering, Institute of Smart City and Intelligent Transportation, Southwest Jiaotong University, Chengdu 611756, China
2.
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China
3.
China Railway Siyuan Survey and Design Group Co., Ltd., Wuhan 430063, China
4.
Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong 999077, China
5.
Shandong Provincial Key Laboratory of Marine Environment and Geological Engineering, Ocean University of China, Qingdao 266100, China
6.
College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
7.
Power China Huadong Engineering Co., Ltd., Hangzhou 311122, China

More Information

Received Date: July 26, 2023
Available Online: July 30, 2024

Graphical Abstract

Abstract

Abstract

The mechanical stratification of soils based on the cone penetration test data (such as soil classification index I_c) is widely applied. However, the soil stratification based on I_c depends on engineering experience, and the subjective uncertainty is prominent. The mechanical stratification of soils is not necessarily consistent with the stratification based on borehole sampling. An efficient optimization identification method based on I_c data and the joint probability density function of mechanical profile parameters of soils is proposed under the framework of the Bayesian learning. The proposed method utilizes a full Gaussian probabilistic model to derive the likelihood function and then optimizes the maxima of the joint probability density function of mechanical profile parameters of soils using the simulated annealing algorithm. Subsequently, the number of soil layers and the associated soil thicknesses or boundaries are obtained by comparing the maxima of the joint probability density function with respect to the different numbers of soil layers. Finally, the rationality and validity of the proposed method are illustrated by a set of CPT data obtained from a subway section in Hangzhou and the simulated data, and the stratification principle and characteristics of the proposed method are illustrated with the identification results of soil profiles. The results show that the calculation efficiency of the proposed method for identifying the mechanical stratification of soils based on the I_c data is significantly improved, and it is suitable for analyzing the CPT data with different sounding depths. The calculation procedure of the proposed approach is relatively simple and is convenient for engineering applications.
- cone penetration test,
- mechanical soil stratification,
- Bayesian method,
- spatial variability

FullText(HTML)

References (31)

References

[1]	BAECHER G B, CHRISTIAN J T. Reliability and Statistics in Geotechnical Engineering[M]. New York: John Wiley & Sons, 2005.
[2]	LAFUERZA S, CANALS M, CASAMOR J L, et al. Characterization of deltaic sediment bodies based on in situ CPT/CPTU profiles: a case study on the Llobregat delta plain, Barcelona, Spain[J]. Marine Geology, 2005, 222: 497-510.
[3]	MIAO C, CAO Z J, XIAO T, et al. BayLUP: a Bayesian framework for conditional random field simulation of the liquefaction-induced settlement considering statistical uncertainty and model error[J]. Gondwana Research, 2023, 123: 140-163. doi: 10.1016/j.gr.2022.10.020
[4]	蒋维三, 叶舟, 郑华平, 等. 杭州湾地区第四系浅层天然气的特征及勘探方法[J]. 天然气工业, 1997, 17(3): 20-23. JIANG Weisan, YE Zhou, ZHENG Huaping, et al. Quaternary shallow gas characteristics in Hangzhou bay and the exploration method, [J]. Natural Gas Industry, 1997, 17(3): 20-23. (in Chinese)
[5]	ROBERTSON P K. Soil classification using the cone penetration test[J]. Canadian Geotechnical Journal, 1990, 27(1): 151-158. doi: 10.1139/t90-014
[6]	JEFFERIES M G, DAVIES M P. Use of CPTu to estimate equivalent SPT N60[J]. Geotechnical Testing Journal, 1993, 16(4): 458-468. doi: 10.1520/GTJ10286J
[7]	张诚厚, 施健, 戴济群. 孔压静力触探试验的应用[J]. 岩土工程学报, 1997, 19(1): 50-57. doi: 10.3321/j.issn:1000-4548.1997.01.008 ZHANG Chenghou, SHI Jian, DAI Jiqun. The application of piezocone tests in China[J]. Chinese Journal of Geotechnical Engineering, 1997, 19(1): 50-57. (in Chinese) doi: 10.3321/j.issn:1000-4548.1997.01.008
[8]	ROBERTSON P K, WRIDE C F. Evaluating cyclic liquefaction potential using the cone penetration test[J]. Canadian Geotechnical Journal, 1998, 35(3): 442-459. doi: 10.1139/t98-017
[9]	ROBERTSON P K. Interpretation of cone penetration tests—a unified approach[J]. Canadian Geotechnical Journal, 2009, 46(11): 1337-1355. doi: 10.1139/T09-065
[10]	刘松玉, 蔡国军, 邹海峰. 基于CPTU的中国实用土分类方法研究[J]. 岩土工程学报, 2013, 35(10): 1765-1776. http://cge.nhri.cn/article/id/15294 LIU Songyu, CAI Guojun, ZOU Haifeng. Practical soil classification methods in China based on piezocone penetration tests[J]. Chinese Journal of Geotechnical Engineering, 2013, 35(10): 1765-1776. (in Chinese) http://cge.nhri.cn/article/id/15294
[11]	岩土工程勘察规范: GB 50021—2001[S]. 北京: 中国建筑工业出版社, 2004. Code for Investigation of Geotechnical Engineering: GB 50021—2001[S]. Beijing: China Architecture & Building Press, 2004. (in Chinese)
[12]	ROBERTSON P K. Soil behaviour type from the CPT: an update[C]// 2nd International Symposium on Cone Penetration Testing. Huntington Beach, 2010.
[13]	WANG Y, HUANG K, CAO Z J. Probabilistic identification of underground soil stratification using cone penetration tests[J]. Canadian Geotechnical Journal, 2013, 50(7): 766-776. doi: 10.1139/cgj-2013-0004
[14]	ZHANG Z J, TUMAY M T. Statistical to fuzzy approach toward CPT soil classification[J]. Journal of Geotechnical and Geoenvironmental Engineering, 1999, 125(3): 179-186. doi: 10.1061/(ASCE)1090-0241(1999)125:3(179)
[15]	HEGAZY Y A, MAYNE P W. Objective site characterization using clustering of piezocone data[J]. Journal of Geotechnical and Geoenvironmental Engineering, 2002, 128(12): 986-996. doi: 10.1061/(ASCE)1090-0241(2002)128:12(986)
[16]	MOLINA-GÓMEZ F, VIANA DA FONSECA A, FERREIRA C, et al. Defining the soil stratigraphy from seismic piezocone data: a clustering approach[J]. Engineering Geology, 2021, 287: 106111. doi: 10.1016/j.enggeo.2021.106111
[17]	蔡国军, 刘松玉, 童立元, 等. 基于聚类分析理论的CPTU土分类方法研究[J]. 岩土工程学报, 2009, 31(3): 416-424. doi: 10.3321/j.issn:1000-4548.2009.03.018 CAI Guojun, LIU Songyu, TONG Liyuan, et al. Soil classification using CPTU data based upon cluster analysis theory[J]. Chinese Journal of Geotechnical Engineering, 2009, 31(3): 416-424. (in Chinese) doi: 10.3321/j.issn:1000-4548.2009.03.018
[18]	CHING J, WANG J S, 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. doi: 10.1139/cgj-2015-0027
[19]	CAO Z J, ZHENG S, LI D Q, et al. Bayesian identification of soil stratigraphy based on soil behaviour type index[J]. Canadian Geotechnical Journal, 2019, 56(4): 570-586. doi: 10.1139/cgj-2017-0714
[20]	XIAO T, ZOU H F, YIN K S, et al. Machine learning-enhanced soil classification by integrating borehole and CPTU data with noise filtering[J]. Bulletin of Engineering Geology and the Environment, 2021, 80(12): 9157-9171. doi: 10.1007/s10064-021-02478-x
[21]	WANG X R, WANG H, LIANG R Y, et al. A semi-supervised clustering-based approach for stratification identification using borehole and cone penetration test data[J]. Engineering Geology, 2019, 248: 102-116. doi: 10.1016/j.enggeo.2018.11.014
[22]	BISHOP C M. Pattern Recognition and Machine Learning[M]. New York: Springer, 2006.
[23]	ROBERTSON P K, CAMPANELLA R G. Interpretation of cone penetration tests. Part I: Sand[J]. Canadian Geotechnical Journal, 1983, 20(4): 718-733. doi: 10.1139/t83-078
[24]	AHMADI M M, ROBERTSON P K. Thin-layer effects on the CPT q_c measurement[J]. Canadian Geotechnical Journal, 2005, 42(5): 1302-1317. doi: 10.1139/t05-036
[25]	FRÜHWIRTH-SCHNATTER S. Finite Mixture and Markov Switching Models[M]. New York: Springer, 2006
[26]	PHOON K K, QUEK S T, AN P. Identification of statistically homogeneous soil layers using modified Bartlett statistics[J]. Journal of Geotechnical and Geoenvironmental Engineering, 2003, 129(7): 649-659. doi: 10.1061/(ASCE)1090-0241(2003)129:7(649)
[27]	张征, 刘淑春, 鞠硕华. 岩土参数空间变异性分析原理与最优估计模型[J]. 岩土工程学报, 1996, 18(4): 40-47. doi: 10.3321/j.issn:1000-4548.1996.04.007 ZHANG Zheng, LIU Shuchun, JU Shuohua. The optimum estimation model and the principle of spatial variability analysis of rock and soil parameters[J]. Chinese Journal of Geotechnical Engineering, 1996, 18(4): 40-47. (in Chinese) doi: 10.3321/j.issn:1000-4548.1996.04.007
[28]	曹子君, 胡超, 苗聪, 等. 基于分层贝叶斯学习的滨海软土地层高效识别方法[J]. 地球科学, 2023, 48(5): 1730-1741. CAO Zijun, HU Chao, MIAO Cong, et al. Efficient identification method of coastal soft soil stratum based on hierarchical Bayesian learning[J]. Earth Science, 2023, 48(5): 1730-1741. (in Chinese)
[29]	MURPHY K P. Machine Learning: a Probabilistic Perspective[M]. Combridge: MIT Press, 2012.
[30]	GOFFE W L, FERRIER G D, ROGERS J. Global optimization of statistical functions with simulated annealing[J]. Journal of Econometrics, 1994, 60(1/2): 65-99.
[31]	DELAHAYE D, CHAIMATANAN S, MONGEAU M. Simulated annealing: from basics to applications[M]// International Series in Operations Research & Management Science. Cham: Springer International Publishing, 2018: 1-35.

Supplements (1)

Supplements
Other Related Supplements
- PDF format
  13-202502-G23-0715⼀⽂审稿意见与作者答复 Download(1225KB)

Cited By

Cited by

Periodical cited type(6)

1.	刘红，陈怡馨，刘汉龙，孙增春，肖杨. 土体热力学性质试验研究进展. 中国科学:技术科学. 2024(01): 1-14 .
2.	杨丰华，马艳霞，高英，李忠林. 冻融循环及各向异性对原状黄土动力特性的影响研究. 水利水电技术(中英文). 2024(02): 167-179 .
3.	王弘起，邱明明，孙杰龙，李大卫，郑隆君，田宇轩. 物理性质对黄土抗剪强度指标的影响规律. 科技通报. 2023(04): 38-44+61 .
4.	邱明明，郑隆君，田宇轩，周恒祎，李明泽. 延安新区原状黄土强度各向异性试验研究. 工程勘察. 2023(09): 14-20 .
5.	邱明明，许丹，孙杰龙，李盛斌，张志远，李月文，兰旭峰. 增减湿效应对延安Q_3黄土抗压强度的影响规律. 延安大学学报(自然科学版). 2023(03): 115-120 .
6.	刘中原，李徳武，张文博，张博文. 干密度和含水率对黄土各向异性差值的影响. 公路工程. 2023(06): 143-146+168 .

Other cited types(8)

Get Citation

PDF

XML

Article views PDF downloads Cited by(14)

Efficient optimization identification method for soil stratification based on cone penetration test and joint posterior distribution of variable dimensionality

Abstract

References

Supplements

Other Related Supplements

PDF format

Cited by

Periodical cited type(6)

Other cited types(8)

Catalog

Related

Export File

Citation

Format

Content