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Volume 41 Issue 7
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LIU Xiao-yan, CAI Guo-jun, ZOU Hai-feng, LI Xue-peng, LIU Song-yu. 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. DOI: 10.11779/CJGE201907011
Citation: LIU Xiao-yan, CAI Guo-jun, ZOU Hai-feng, LI Xue-peng, LIU Song-yu. 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. DOI: 10.11779/CJGE201907011
shu

Prediction of stress history and strength of cohesive soils based on CPTU and data fusion techniques

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

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  • Received Date: May 03, 2018
  • Published Date: July 24, 2019
    Graphical Abstract
    • Abstract
  • The overconsolidation ratio (OCR) and the undrained shear strength (Su) are the basic mechanical parameters of soils, which can influence the deformation analysis and strength calculation of soils. A prediction model for OCR and Su of typical clay in Jiangsu Province is proposed by using the data fusion technique and the data of piezocone penetration test (CPTU). The feasibility of the prediction model is analyzed by using the feature-level data fusion techniques (regression tree, model tree) and decision-level data fusion techniques (bagging, stacking). The predicted OCR and Su, the reference values obtained by the laboratory tests and the estimated values obtained by the existing calculation methods are compared and analyzed. The results show that the predicted results of the model tree are better than those of the regression tree. The decision and fusion algorithms can improve the predicted results of the regression tree, but they have little influences on the predicted results of the model tree. The superimposed regression tree and model tree can make the predicted Su better than that of the regression tree, but worse than that of the model tree. For several data fusion models, the predicted OCR is approximately close. The regression tree model is slightly better than other data fusion models in predicting the OCR. Compared with other prediction methods, the data fusion model can better predict the OCR and Su.
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    • References (21)
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