Journal of Jilin University(Earth Science Edition) ›› 2019, Vol. 49 ›› Issue (3): 746-754.doi: 10.13278/j.cnki.jjuese.20170238

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Prediction of Consolidation Coefficient Based on Neighborhood Rough Set and Support Vector Machine

Yin Chao1,2, Zhou Aihong2, Yuan Ying2, Wang Shuaiwei3   

  1. 1. School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China;
    2. School of Prospecting Technology & Engineering, Hebei GEO University, Shijiazhuang 050031, China;
    3. Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Science, Shijiazhuang 050061, China
  • Received:2017-12-18 Online:2019-06-03 Published:2019-06-03
  • Supported by:
    Supported by National Natural Science Foundation of China(41301015) and Key Projects of Hebei Province Education Department(ZD2015073,ZD2016038)

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Abstract: The prediction model of soft soil consolidation coefficient in Hutuo River was established by neighborhood rough set (NRS) and support vector machine(SVM). Based on the modified osmotic oedometer, the consolidation coefficient was determined under different pressures. The soil indices were determined by laboratory tests,and the attributes were reduced by NRS method. Subsequently, by taking the reduced indices as the influencing factors; the prediction model of consolidation coefficient by SVM and back propagation neural network (BPNN) was used to predict the consolidation coefficient of unknown samples,and the results were compared with the measured values. The results show that:the consolidation coefficient can be determined by formula method. The consolidation coefficient prediction model established by SVM and BPNN can be applied to predict the consolidation coefficients, and the prediction accuracy of SVM is about 10% higher than that of BPNN. The results are validated by the standard solution. In summary, the method presented is based on experimental data, the prediction model of consolidation coefficient is established through easily accessible factors, and can predict the consolidation coefficient of other unknown points in this area.

Key words: consolidation coefficient, prediction model, support vector machine, neighbourhood rough set, osmotic oedometer

CLC Number: 

  • TU411
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