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Volume 43 Issue S2
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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

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  • 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.
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    • References (12)
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