Geomechanics and Engineering

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Consolidation settlement of soil foundations containing organic matters subjected to embankment load

  • Feng, Ruiling (School of Civil Engineering, Beijing Jiaotong University) ;
  • Wang, Liyang (School of Civil Engineering, Beijing Jiaotong University) ;
  • Wei, Kang (School of Civil Engineering, Beijing Jiaotong University) ;
  • Zhao, Jiacheng (School of Civil Engineering, Beijing Jiaotong University)
  • Received : 2020.03.19
  • Accepted : 2020.12.22
  • Published : 2021.01.10
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Abstract

Peatland is distributed in China widely, and organic matters in soil frequently induce problems in the construction and maintenance of highway engineering due to the high permeability and compressibility. In this paper, a selected site of Dali-Lijiang expressway was surveyed in China. A numerical model was built to predict the settlement of the foundation of the selected section employing the soft soil creep (SSC) model in PLAXIS 8.2. The model was subsequently verified by the result of field observance. Consequently, the parameters of 17 types of soils from different regions in China with organic contents varying from 1.1-74.9% were assigned to the numerical model to study the settlement characteristics. The calculated results showed that the duration of primary consolidation and proportion of primary settlement in the total settlement decreased with increasing organic content. Two empirical equations, for total consolidation settlement and secondary settlement, were proposed using multiple linear regression based on the calculated results from the numerical models. The analysis results of the significances of certain soil parameters demonstrated that the natural compression index, secondary compression index, cohesion and friction angle have significant linear relevance with both the total settlement and secondary settlement, while the initial coefficient of permeability exerts significant influence on the secondary settlement only.

Keywords

Acknowledgement

This work was supported by the National Science Foundation of China (Grant number 51778048).

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