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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Al–Khafaji AWN, Andersland OB (1992) Equations for compression index approximation. J Geotech Eng 118(1):148–153
Azzouz AS, Krizek RJ, Corotis RB (1976) Regression analysis of soil compressibility. Soils Found 16(2):19–29
Bae WS, Kwon Y (2017) Prediction of consolidation parameter using multiple regression analysis. Mar Georesour Geotechnol 35(5):643–652
Bowles JE (1979) Physical and geotechnical properties of soils. McGraw–Hill Book Company, New York
Briaud J, Tucker LM (1988) Measured and predicted axial response of 98 piles. J Geotech Eng 114(9):984–1001
Chen XP, Huang GY, Liang ZS (2003) Study on soft soil properties of the Pearl River Delta. Chin J Rock Mech Eng 22(1):137–141 (in Chinese)
Cherubini C, Greco VR (1998) A comparison between “measured” and “calculated” values in geotechnics. Proceedings of the Workshop Probamat–21st Century: Probabilities and Materials. Kluwer Academic Publishers, Dordrecht, Netherlands, pp 481–498
Cherubini C, Orr TLL (2000) A rational procedure for comparing measured and calculated values in geotechnics. In: Yokohama IS, Nakase A, Tsuchida T (eds) Proceedings of the international symposium on coastal geotechnical engineering in practice, AA Balkema, Rotterdam, vol. 1, pp 261–265
Gao YB, Zhang SB, Ge XN (2017) Comparisons of compression index of Chinese coastal soft clay and soils from foreign regions. Rock Soil Mech 38(09):2713–2720 (in Chinese)
Giasi CI, Cherubini C, Paccapelo F (2003) Evaluation of compression index of remolded clays by means of Atterberg limits. Bull Eng Geol Environ 62(4):333–340
Herrero OR (1983) Universal compression index equation; closure. J Geotech Eng Div 109(5):755–761
Hough BK (1957) Basic soils engineering. The Ronald Press Company, New York, pp 114–115
Koppula SD (1981) Statistical estimation of compression index. Geotech Test J 4(2):68–73
Mayne PW (1980) Cam–clay predictions of undrained strength. J Geotech Eng Div 106(11):1219–1242
Nishida Y (1956) A brief note on compression index of soil. J Soil Mech Found Div 82(3):1–14. https://doi.org/10.1016/j.coastaleng.2018.04.014
Onyejekwe S, Kang X, Ge L (2015) Assessment of empirical equations for the compression index of fine–grained soils in Missouri. Bull Eng Geol Environ 74(3):705–716
Orr TLL, Cherubini C (2003) Use of the ranking distance as an index for assessing the accuracy and precision of equations for the bearing capacity of piles and at–rest earth pressure coefficient. Can Geotech J 40:1200–1207
Ozer M, Isik NS, Orhan M (2008) Statistical and neural network assessment of the compression index of clay–bearing soils. Bull Eng Geol Environ 67:537–545
Park JH, Koumoto T (2004) New compression index equation. J Geotech Geoenviron 130(2):223–226
Pradeepkumar D, Ravi V (2017) Forecasting financial time series volatility using particle swarm optimization trained quantile regression neural network. Appl Soft Comput 58(9):35–52
Que J, Wang Q, Chen J, Shi B, Meng Q (2008) Geotechnical properties of the soft soil in Guangzhou College City. Bull Eng Geol Environ 67(4):479–483
Theil H (1966) Applied economic forecasting. North–Holland Pub. Co., Amsterdam
Xia YF, Wu DH, Wen JH (2008) Statistic analysis of physical and mechanical indices of soft soil in Zhujiang Delta. JHTRD 25(1):47–50 (in Chinese). http://manu27.magtech.com.cn/Jwk_gljtkj_en/EN/column/column4290.shtml
Yilmaz I (2006) Indirect estimation of the swelling percent and a new classification of soils depending on liquid limit and cation exchange capacity. Eng Geol 85(3):295–301
Yoon GL, Kim BT (2006) Regression analysis of compression index for Kwangyang marine clay. KSCE J Civ Eng 10(6):415–418
Yoon GL, Kim BT, Jeon SS (2004) Empirical correlations of compression index for marine clay from regression analysis. Can Geotech J 41(6):1213–1221
Zhao YM, Jiang HH, Zhang HM (2004) Deformation parameters of Shenzhen soft clay. China Railway Science 03:41–44 (in Chinese)
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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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
- Muddy soil
- Compression index
- Evaluation methods
- Regional empirical equation
- Statistics relationship
- Regression analysis