Assessment of empirical equations of the compression index of muddy clay: sensitivity to geographic locality

Abstract

The physical and mechanical indices of soft soils have regional characteristics, and the engineering properties are very different under diverse geological conditions. Empirical equations provide a quick and effective method to calculate the compression index using other physical indices that are easily obtained. However, it is often unsatisfactory to calculate the compression index for a special region using the existing empirical equations. Hence, there is a need to propose regional empirical equations on the basis of a special research data for calculating the compression index. The validity of existing empirical equations for the soft soil in the Jiangmen region was evaluated using measured data. The results show that the equations proposed by Gao et al. (Rock Soil Mech 38(09):2713–2720, 2017) and Al–Khafaji and Andersland (J Geotech Eng 118(1):148–153, 1992) are superior to other existing single-variable and multi-variable empirical equations for the Jiangmen region; the values of ranking distance are 0.432 and 0.430, respectively. In addition, a new regional empirical equation for Jiangmen is proposed, utilizing a regression analysis of the measured data. The corresponding value of ranking distance is 0.320. The new equation is proven to be more accurate than the existing single- and multi-variable empirical equations.

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Funding

The support received from the Key Program of Natural Science Foundation of China (51774020) and the Beijing Training Project for the Leading Talent in S & T (Z151100000315014) is gratefully acknowledged.

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Correspondence to Qun Li or Yang Liu.

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Huang, C., Li, Q., Wu, S. et al. Assessment of empirical equations of the compression index of muddy clay: sensitivity to geographic locality. Arab J Geosci 12, 122 (2019). https://doi.org/10.1007/s12517-019-4276-5

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Keywords

  • Muddy soil
  • Compression index
  • Evaluation methods
  • Regional empirical equation
  • Statistics relationship
  • Regression analysis