Advertisement

Journal of Central South University

, Volume 25, Issue 12, pp 2841–2856 | Cite as

A new procedure for determining dry density of mixed soil containing oversize gravel

  • Hamed Farshbaf AghajaniEmail author
  • Masoud Ghodrati Yengejeh
  • Amirmohammad Karimzadeh
  • Hossein Soltani-Jigheh
Article
  • 5 Downloads

Abstract

This paper presents a novel computational procedure for the maximum dry density of mixed soils containing oversize particles. At first, the large-scale compaction test data for mixed soils are analyzed by an artificial neural network to determine the main factors affecting the compaction. These factors are then imposed on a genetic programming method and a new mathematical equation emerges. The new equation has more conformity with the experimental data in comparison with the previous correction methods. Besides, the mixed soil dry density is associated with most base soil and oversize fraction specifications. With regard to the sensitivity analyses, if the mixed soil contains high percentages of oversize fraction, the mixed soil composition is governed by the specification of oversized grains, such as specific gravity and the maximum grain size and by increasing these factors, the mixed soil dry density is increased. In mixed soil with a low content of oversize, the base soil specification mainly controls the compaction behavior of mixed soil. Furthermore, if the base soil is inherently compacted with greater dry density, adding the oversize slightly improves the mixed soil dry density. In contrast, adding oversized grains to the base soil with a lower dry density produces a mixed soil with greater dry density. By increasing the maximum grain size difference between the oversize fraction and base soil, the dry density of mixed soil is enhanced.

Key words

mixed soil oversize compaction genetic programming artificial neural network 

一种测定超大砾石混合土干密度的新方法

摘要

提出一种计算超大颗粒混合土最大干密度的新方法。首先,利用人工神经网络对混合土的大规 模压实试验数据进行分析,确定影响压实度的主要因素。然后,将这些因素加到遗传规划方法中,得 到一个新的数学方程。与以往的修正方法相比,新方程与实验数据更符合。此外,混合土干密度与大 多数基层土和超大颗粒的比例有关。在敏感性分析方面,当混合土中含有较大比例的超细粉土时,混 合土的组成受超大颗粒的规格(如比重和最大粒径)的控制,而随着这些因素的增加,混合土壤的干密 度也随之增大。在超大颗粒含量较低的混合土中,基层土主要控制混合土的压实特性。此外,如果基 土以较高的干密度压实,加入过大的颗粒会稍微改善混合土的干密度。相反,在干密度较低的基层土 壤中添加过大的颗粒会产生干密度较大的混合土。通过增大超细粉土与基土的最大粒径差,提高了混 合土的干密度。

关键词

混合土 超大尺寸 压实 遗传规划 人工神经网络 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    ASTM D1557-00 standard test method for laboratory compaction characteristics of soil using modified effort [S]. West Conshohocken, Pennsylvania, USA: American Society for Testing and Materials.Google Scholar
  2. [2]
    ASTM D698-00 standard test methods for laboratory compaction characteristics on soil using standard effort [S]. West Conshohocken, Pennsylvania, USA: American Society for Testing and Materials.Google Scholar
  3. [3]
    ASTM D4253-02 standard test method for maximum index density and unit weight of soils using a vibratory table [S]. West Conshohocken, Pennsylvania, USA: American Society for Testing and Materials.Google Scholar
  4. [4]
    USCOLD. Construction testing of embankment materials containing large particles [R]. United States Committee on Large Dams, 1988.Google Scholar
  5. [5]
    HOUSTON S L, WALSH K D. Comparison of rock correction methods for compaction of clayey soils [J]. Journal of Geotechnical Engineering, 1993, 119(4): 763–778.CrossRefGoogle Scholar
  6. [6]
    WINTER M. The effect of stone content on the determination of acceptability for earthworking [C]//Engineered Fills Proceedings of the Conference' Engineered Fills'93'. Newcastle Upon Tyne, 1993.Google Scholar
  7. [7]
    DONAGHE R, TOWNSEND F. Scalping and replacement effects on the compaction characteristics of earth-rock mixtures [M]//Soil Specimen Preparation for Laboratory Testing. ASTM International, 1976.CrossRefGoogle Scholar
  8. [8]
    GARGA V K, MADUREIRA C J. Compaction characteristics of river terrace gravel [J]. Journal of Geotechnical Engineering, 1985, 111(8): 987–1007.CrossRefGoogle Scholar
  9. [9]
    NOORANY I. Discussion of “relative compaction of fill having oversize particles” by Robert W. Day (October, 1989, Vol. 115, No. 10) [J]. Journal of Geotechnical Engineering, 1991, 117(10): 1635–1637.Google Scholar
  10. [10]
    WINTER M, HÓLMGEIRSDÓTTIR T. The effect of large particles on acceptability determination for earthworks compaction [J]. Quarterly Journal of Engineering Geology and Hydrogeology, 1998, 31(3): 247–268.CrossRefGoogle Scholar
  11. [11]
    DONAGHE R, TOWNSEND F. Compaction characteristics of earth-rock mixtures report 1: Vicksburg silty clay and DeGray dam clayey sandy gravel [R]. Vicksburg, Missouri: U.S. Army Engineer Waterways Experiment Station, Report No. S-73-25, May 1973.CrossRefGoogle Scholar
  12. [12]
    DONAGHE R T, TORREY III V. Strength and deformation properties of earth-rock mixtures [R]. DTIC Document, 1985.Google Scholar
  13. [13]
    TORREY III V H, DONAGHE R T. Compaction characteristics of earth-rock mixtures [R]. DTIC Document, 1991.CrossRefGoogle Scholar
  14. [14]
    ZIEGLER E J. Effect of materials retained on the No. 4 sieve on the compaction test of soil [J]. Proceedings, Highway Research Board, 1948, 28: 409–414.Google Scholar
  15. [15]
    GUERRERO A M A. Effects of the soil properties on the maximum dry density obtained from the standard proctor test [D]. Florida, USA: University of Central Florida Orlando, 2004.Google Scholar
  16. [16]
    ASTM D 4718–87 standard practice for correction of unit weight and water content for soils containing oversize particles [S]. West Conshohocken, Pennsylvania, USA: American Society for Testing and Materials.Google Scholar
  17. [17]
    NAVFAC M. Design manual—Soil foundations and earth structures [R]. NAVFAC DM-7, March 1971.Google Scholar
  18. [18]
    DAY R W. Relative compaction of fill having oversize particles [J]. Journal of Geotechnical Engineering, 1989, 115(10): 1487–1491.CrossRefGoogle Scholar
  19. [19]
    HSU T, SAXENA S K. A general formula for determining density of compacted soils with oversize particles [J]. Soils and Foundations, 1991, 31(3): 91–96.CrossRefGoogle Scholar
  20. [20]
    TORREY V H, DONAGHE R T. Compaction control of earth-rock mixtures: A new approach [J. Geotechnical Testing Journal, 1994, 17(3): 371–386.CrossRefGoogle Scholar
  21. [21]
    HOUSTON W, MERRIMAN J. Research on compaction control testing for gravelly soils [R]. Earth Research Program, DTIC Document, 1963.Google Scholar
  22. [22]
    SHELLEY T L, DANIEL D E. Effect of gravel on hydraulic conductivity of compacted soil liners [J]. Journal of Geotechnical Engineering, 1993, 119(1): 54–68.CrossRefGoogle Scholar
  23. [23]
    GORDON B, HAMMOND W, MILLER R. Effect of rock content on compaction characteristics of clayey gravel [M]//Compaction of Soils. ASTM International, 1965.CrossRefGoogle Scholar
  24. [24]
    INDRAWAN I, RAHARDJO H, LEONG E C. Effects of coarse-grained materials on properties of residual soil [J]. Engineering Geology, 2006, 82(3): 154–164.CrossRefGoogle Scholar
  25. [25]
    SIMONI A, HOULSBY G T. The direct shear strength and dilatancy of sand–gravel mixtures [J]. Geotechnical & Geological Engineering, 2006, 24(3): 523–549.CrossRefGoogle Scholar
  26. [26]
    HAMIDI A, YAZDANJOU V, SALIMI N. Shear strength characteristics of sand-gravel mixtures [J]. International Journal of Geotechnical Engineering, 2009, 3(1): 29–38.CrossRefGoogle Scholar
  27. [27]
    HAM T G, NAKATA Y, ORENSE R P, HYODO M. Influence of gravel on the compression characteristics of decomposed granite soil [J]. Journal of Geotechnical and Geoenvironmental Engineering, 2010, 136(11): 1574–1577.CrossRefGoogle Scholar
  28. [28]
    CHOOBBASTI A J, GHALANDARZADEH A, ESMAEILI M. Experimental study of the grading characteristic effect on the liquefaction resistance of various graded sands and gravelly sands [J]. Arabian Journal of Geosciences, 2014, 7(7): 2739–2748.CrossRefGoogle Scholar
  29. [29]
    CHEN J, LUO Q, JIANG L, ZHANG L, ZHAO M. An oversize correction method of dry density for non-cohesive soils filling the embankment of high-speed railway [J]. Science China: Technological Sciences, 2015, 58(2): 211–218.CrossRefGoogle Scholar
  30. [30]
    SHAHIN M A, MAIER H R, JAKSA M B. Data division for developing neural networks applied to geotechnical engineering [J]. Journal of Computing in Civil Engineering, 2004, 18(2): 105–114.CrossRefGoogle Scholar
  31. [31]
    IKIZLER S B, VEKLI M, DOGAN E, AYTEKIN M, KOCABAS F. Prediction of swelling pressures of expansive soils using soft computing methods [J]. Neural Computing and Applications, 2012, 24(2): 473–485.CrossRefzbMATHGoogle Scholar
  32. [32]
    TSAI H C, TYAN Y Y, WU Y W, LIN Y H. Determining ultimate bearing capacity of shallow foundations using a genetic programming system [J]. Neural Computing and Applications, 2012, 23(7, 8): 2073–2084.Google Scholar
  33. [33]
    LI Tian, LI Yong, YANG Xiao. Rock burst pre diction based on genetic algorithms and extreme learning ma chine [J]. Journal of Central South University, 2017, 24(9): 2105–2113. DOI: https://doi.org/10.1007/s11771-017-3619-1.CrossRefGoogle Scholar
  34. [34]
    SEZER A. Simple models for the estimation of shearing resistance angle of uniform sands [J]. Neural Computing and Applications, 2013, 22(1): 111–123.CrossRefGoogle Scholar
  35. [35]
    AGHAJANI H F, SALEHZADEH H, SHAHNAZARI H. Application of artificial neural network for calculating anisotropic friction angle of sands and effect on slope stability [J]. Journal of Central South University, 2015, 22(5): 1878–1891.CrossRefGoogle Scholar
  36. [36]
    DEMUTH H, BEALE M, WORKS M. MATLAB: Neural network toolbox: User’s guide [EB/OL]. [1992]. Math Works. www.mathworks.com.Google Scholar
  37. [37]
    SMITH M. Neural networks for statistical modeling [M]. Thomson learning. New York, USA: John Wiley Sons, Inc, 1993: 59.Google Scholar
  38. [38]
    GARSON G D. Interpreting neural-network connection weights [J]. AI Expert, 1991, 6(4): 46–51.Google Scholar
  39. [39]
    KOZA J R. Genetic programming: on the programming of computers by means of natural selection [M]. MIT Press, 1992.zbMATHGoogle Scholar
  40. [40]
    WAGNER S, AFFENZELLER M. Heuristiclab: A generic and extensible optimization environment [M]. Adaptive and Natural Computing Algorithms Springer, 2005: 538–541.Google Scholar
  41. [41]
    DRNEVICH V P, EVANS A C, PROCHASKA A B. A study of effective soil compaction control of granular soils [R]. FHWA/IN/JTRP-2007/12. U.S. Department of Transportation Federal Highway Administration, 2007.CrossRefGoogle Scholar
  42. [42]
    BAKER T H W. Transportation, preparation, and storage of frozen soil samples for laboratory testing [M]//Soil Specimen Preparation for Laboratory Testing. ASTM International, 1976.CrossRefGoogle Scholar
  43. [43]
    BARNES G. The moisture condition value and compaction of stony clays [C]//Compaction Technology Conference, 1987. London, United Kingdom, 1988.Google Scholar

Copyright information

© Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Civil Engineering, Faculty of EngineeringAzarbaijan Shahid Madani UniversityTabrizIran

Personalised recommendations