首页|A back analysis scheme for refined soil stratification based on integrating borehole and CPT data
A back analysis scheme for refined soil stratification based on integrating borehole and CPT data
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NSTL
万方数据
维普
Utilizing both borehole and Cone Penetration Testing(CPT)data in soil stratification helps to get more convincing soil stratification results.However,the soil classification results revealed by borehole(Unified Soil Classification System,USCS)and CPT tests(soil behavior type,SBT)are commonly not con-sistent.This study proposes a feasible solution to integrate the borehole and CPT data with the tree-based method.The tree-based method is naturally suitable for soil stratification tasks as it aims to divide the subsurface space into several clusters based on the similarities of the soil types.A novel boundary dic-tionary method is proposed to enhance the model performance on complex soil layer conditions.A prob-abilistic mapping matrix between the USCS-SBT system is built based on a collected municipal database with collocated borehole and CPT data.The optimal soil stratification results can be selected based on considering multiple borehole information and pruning the structure of trees.The structure of the trees can be optimized in a back analysis perspective with the Sequential Model-Based Global Optimization(SMBO)algorithm which aims to maximize the possibility of observing the borehole information based on the USCS-SBT probabilistic mapping matrix.The uncertainties of the optimal soil stratification results can be estimated based on a weighted Gini index method.The performance of the proposed method is validated based on a real case in New Zealand with a cross-validation method.The results indicate that the proposed method is robust and effective.
Jiawei Xie、Cheng Zeng、Jinsong Huang、Yuting Zhang、Jianlin Lu
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Discipline of Civil,Surveying & Environmental Engineering,Priority Research Centre for Geotechnical Science & Engineering,The University of Newcastle,Callaghan,NSW 2308,Australia
Australian Government through the Australian Research Council's Discovery Projects funding scheme国家自然科学基金国家自然科学基金
NETL
NSTL
万方数据
维普
