Advanced Search
  • 全国中文核心期刊
  • 中国科技核心期刊
  • 美国工程索引（EI）收录期刊
  • Scopus数据库收录期刊
Volume 43 Issue S2
Turn off MathJax
Article Contents
Abstract
References
Related Articles
ZHAO Ze-ning, DUAN Wei, CAI Guo-jun, LIU Song-yu, CHANG Jian-xin, FENG Hua-lei. Evaluation of stress history of clays based on intelligent CPTU machine learning algorithm[J]. Chinese Journal of Geotechnical Engineering, 2021, 43(S2): 104-107. DOI: 10.11779/CJGE2021S2025
Citation: ZHAO Ze-ning, DUAN Wei, CAI Guo-jun, LIU Song-yu, CHANG Jian-xin, FENG Hua-lei. Evaluation of stress history of clays based on intelligent CPTU machine learning algorithm[J]. Chinese Journal of Geotechnical Engineering, 2021, 43(S2): 104-107. DOI: 10.11779/CJGE2021S2025
shu

Evaluation of stress history of clays based on intelligent CPTU machine learning algorithm

  • 1.

    Institute of Geotechnical Engineering, Southeast University, Nanjing 211189, China

  • 2.

    College of Civil Engineering, Taiyuan University of Technology, Taiyuan 030024, China

More Information
  • Received Date: August 15, 2021
  • Available Online: December 05, 2022
    Graphical Abstract
    • Abstract
  • The stress history is an important index to measure the stability and deformation characteristics of soils, which is often expressed by the overconsolidation ratio (OCR). Based on the CPTU dataset of Jiangsu Province, and taking the laboratory oedometer test data as the reference values, the stress history is evaluated using the multiple adaptive regression splines (MARS) and adaptive fuzzy neural network (ANFIS) algorithms. Then, the results are compared with the reference values and the estimated results of the traditional CPTU method. Finally, the sensitivity analysis is carried out to study the effect of input parameters. The results show that both the MARS model and the ANFIS model can accurately predict the OCR, and the performance is significantly better than that of the traditional CPTU model. Moreover, the MARS model performs best among all the models. In engineering practice, the original CPTU test parameters (qt, fs and u2) are recommended as the input variables. The results of sensitivity analysis of the MARS model are consistent with those of theoretical analysis, which further proves the reliability of the MARS model. The proposed intelligent models can more accurately predict the OCR of clays and guide engineering practice.
    • FullText(HTML)
    • References (12)
  • [1]
    MAYNE P W. Determination of OCR in clays by piezocone tests using cavity expansion and critical state concepts[J]. Soils and Foundations, 1991, 31(2): 65-76. doi: 10.3208/sandf1972.31.2_65
    [2]
    刘晓燕, 蔡国军, 邹海峰, 等. 基于CPTU数据融合技术的黏性土应力历史与强度特性评价研究[J]. 岩土工程学报, 2019, 41(7): 1270-1278. https://www.cnki.com.cn/Article/CJFDTOTAL-YTGC201907013.htm

    LIU Xiao-yan, CAI Guo-jun, ZOU Hai-feng, et al. Prediction of stress history and strength of cohesive soils based on CPTU and data fusion techniques[J]. Chinese Journal of Geotechnical Engineering, 2019, 41(7): 1270-1278. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YTGC201907013.htm
    [3]
    ZHAO Z N, DUAN W, CAI G J. A novel PSO-KELM based soil liquefaction potential evaluation system using CPT and Vs measurement[J]. Soil Dynamics and Earthquake Engineering, 2021(150): 106930.
    [4]
    LUNNE T, POWELL J J M, ROBERTSON P K. Cone Penetration Testing in Geotechnical Practice[M]. CRC Press, 2002.
    [5]
    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
    [6]
    GOH A T C, ZHANG W G. An improvement to MLR model for predicting liquefaction-induced lateral spread using multivariate adaptive regression splines[J]. Engineering Geology, 2014, 170(3): 1-10.
    [7]
    KAYA Z. Predicting liquefaction-induced lateral spreading by using neural network and neuro-fuzzy techniques[J]. International Journal of Geomechanics, 2016, 16(4): 04015095. doi: 10.1061/(ASCE)GM.1943-5622.0000607
    [8]
    ZHANG W G, GOH A T C, ZHANG Y M, et al. Assessment of soil liquefaction based on capacity energy concept and multivariate adaptive regression splines[J]. Engineering Geology, 2015, 188: 29-37. doi: 10.1016/j.enggeo.2015.01.009
    [9]
    ZHANG W G, GOH A T C. Multivariate adaptive regression splines and neural network models for prediction of pile drivability[J]. Geoscience Frontiers, 2016, 7(1): 45-52. doi: 10.1016/j.gsf.2014.10.003
    [10]
    MOAYEDI H, RAFTARI M, SHARIFI A, et al. Optimization of ANFIS with GA and PSO estimating α ratio in driven piles[J]. Engineering With Computers, 2020, 36(1): 227-238. doi: 10.1007/s00366-018-00694-w
    [11]
    ZOU H F, LIU S Y, CAI G J, et al. Multivariate correlation analysis of seismic piezocone penetration (SCPTU) parameters and design properties of Jiangsu quaternary cohesive soils[J]. Engineering Geology, 2017, 228(1/2): 11-38.
    [12]
    SY CHEN B, MAYNE P W. Statistical relationships between piezocone measurements and stress history of clays[J]. Canadian Geotechnical Journal, 1996, 33(3): 488-498. doi: 10.1139/t96-070
    • Related Articles

  • Related Articles

    [1]ZHAO Futang, WU Qixin, ZHENG Junjie, ZHENG Yewei. Generalized shear strain-based model for development of excess pore water pressure in saturated sand under anisotropic consolidation[J]. Chinese Journal of Geotechnical Engineering, 2025, 47(2): 315-323. DOI: 10.11779/CJGE20231122
    [2]TANG Zhao-guang, WANG Yong-zhi, WANG Meng-wei, SUN Rui, LIU Yuan-peng, YANG yang. Incremental model for pore water pressure and its applicability in centrifuge modelling[J]. Chinese Journal of Geotechnical Engineering, 2022, 44(S2): 25-29. DOI: 10.11779/CJGE2022S2006
    [3]WANG Zhi-hua, HE Jian, GAO Hong-mei, WANG Bing-hui, SHEN Ji-rong. Dynamic pore water pressure model for liquefiable soils based on theory of thixotropic fluid[J]. Chinese Journal of Geotechnical Engineering, 2018, 40(12): 2332-2340. DOI: 10.11779/CJGE201812023
    [4]WANG Xiang-ying, LIU Han-long, JIANG Qiang, CHEN Yu-min. Field tests on response of excess pore water pressures of liquefaction resistant rigid-drainage pile[J]. Chinese Journal of Geotechnical Engineering, 2017, 39(4): 645-651. DOI: 10.11779/CJGE201704008
    [5]CAO Zhen-zhong, LIU Hui-da, YUAN Xiao-ming. Liquefaction characteristics and mechanism of gravelly soils[J]. Chinese Journal of Geotechnical Engineering, 2016, 38(7): 1165-1174. DOI: 10.11779/CJGE201607001
    [6]ZHOU En-quan, WANG Zhi-hua, CHEN Guo-xing, LÜ Cong. Constitutive model for fluid of post-liquefied sand[J]. Chinese Journal of Geotechnical Engineering, 2015, 37(1): 112-118. DOI: 10.11779/CJGE201501013
    [7]WANG Zhi-hua, LÜ Cong, XU Zhen-wei, ZHOU En-quan, CHEN Guo-xing. Thixotropy induced by vibration pore water pressure of saturated sands under cyclic loadings[J]. Chinese Journal of Geotechnical Engineering, 2014, 36(10): 1831-1837. DOI: 10.11779/CJGE201410010
    [8]WANG Jun, CAI Yuan-qiang, GUO Lin, YANG Fang. Pore pressure and strain development of Wenzhou saturated soft soil under cyclic loading by stages[J]. Chinese Journal of Geotechnical Engineering, 2012, 34(7): 1349-1354.
    [9]WANG Zhi-hua, ZHOU En-quan, CHEN Guo-xing. Fluid characteristics dependent on excess pore water pressure of saturated sand after growth of pore pressure[J]. Chinese Journal of Geotechnical Engineering, 2012, 34(3): 528-533.
    [10]CHEN Guoxing, LIU Xuezhu. Study on dynamic pore water pressure in silty clay interbedded with fine sand of Nanjing[J]. Chinese Journal of Geotechnical Engineering, 2004, 26(1): 79-82.
    • Supplements (0)

  • Cited by

    Periodical cited type(4)

    1. 施静怡，吴能森，刘强. 静压桩在成层地基中挤土效应的可视化研究. 河南城建学院学报. 2024(02): 20-26 .
    2. 胡文强，周航，刘汉龙. XCC桩群桩沉桩挤土效应透明土模型试验研究. 土木与环境工程学报(中英文). 2024(06): 107-115 .
    3. 丁雪涛，潘殿琦，王明威. CPT阻力受土层界面效应影响的数值模拟. 实验室研究与探索. 2023(05): 26-31+36 .
    4. 田波，王昊武，权磊，谢晋德，朱旭伟. 基于CPT试验的多年冻土区路表变形风险评价. 公路交通科技. 2023(09): 1-7+53 .

    Other cited types(3)

Catalog

    Get Citation
    PDF
    XML
    Article views (204) PDF downloads (127) Cited by(7)
    Related
    Copyright © 2010 Editorial By Chinese Journal of Geotechnical Engineering 苏ICP备05007122号-11公安联网备案号：32010602011255
    Editorial Office address：34 Hujuguan,Nanjing 210024,ChinaPostal Code：210024Tel：025-85829534，85829556E-mail: ge@nhri.cn

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return