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Land clayey deposits compressibility investigation using principal component analysis and multiple regression tools

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The settlement and compressibility magnitude of the major clayey and marly sediments in Tebessa area (N-E of Algeria) depends on several geotechnical parameters such as compression Cc and recompression Cs indices. The aim of this study was to investigate the parameters related to soil compressibility through tools of statistical analysis, which save time in comparison to multiply repeated laboratory tests. The study also adopted the principal component analysis (PCA) method to eliminate a number of uncorrelated variables that have no influence on the compressibility magnitude, or their impact is insignificant. The highest mean correlation coefficients were obtained for different contributing parameters. Multiple regression analysis has been performed to obtain the best fit model of the output Cc parameter taking into account the best correlation by adding parameters as regressors to reach the highest coefficient of regression R2 . The final obtained model of the present case study gives the best fit model with R2 of 0.92 which is a better value compared to different published models in the literature (R2 of 0.7 as maximum). The chosen input parameters using PCA combined with multiple regression analysis allow identifying the most important input parameters that noticeably affect the soil compression index, and provide with the best model for estimating the Cc index.
Rocznik
Tom
Strony
95--107
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
  • Echahid Cheikh Larbi Tebessi University, 12000 Tebessa, Algeria
  • Mining Institute, Department of Mines and Geotechnology, Laboratory of Environment El Bachir Ibrahimi 16-32 Tebessa, Algeria
  • Larbi Tebessi University, 12000 Tebessa, Algeria, Mining Institute, Department of Mines and Geotechnology
  • Larbi Tebessi University, 12000 Tebessa, Algeria, Mining Institute, Department of Mines and Geotechnology
  • Larbi Tebessi University, 12000 Tebessa, Algeria, Department of Geology, University of Tebessa, Algeria
Bibliografia
  • Azzouz A.S., Krizek R.J., Corotis R.B. 1976. Regression analysis of soil compressibility. Soils and Foundations, 16(2), 19–29.
  • Barron R.A. 1948. Consolidation of Fine-Grained Soils by Drain Wells by Drain Wells. Transactions of the American Society of Civil Engineers, 113(1), 718–742.
  • Berrah Y., Boumezbeur A., Kherici N., Charef N. 2018 . Application of dimensional analysis and regression tools to estimate swell pressure of expansive soil in Tebessa (Algeria). Bulletin of Engineering Geology and the Environment, 77(3), 1155–1165.
  • Berrah Y., Brahmi S., Charef N., Boumezbeur A. 2021. Swelling Clay Parameters Investigation Using Design of Experiments (A Case Study). Engineering Geology. IntechOpen.
  • Bharat T.V., Das D.S., Sahu R.K. 2020. Prediction of compressibility behavior of clayey soils of different plasticity for containment applications at large consolidation pressures. Journal of Hazardous, Toxic, and Radioactive Waste, 24(1), 04019036.
  • Borůvka L., Vacek O., Jehlička J. 2005. Principal component analysis as a tool to indicate the origin of potentially toxic elements in soils. Geoderma, 128 (3–4), 289–300.
  • Bowles J.E. 1989. Physical and Geotechnical Properties of Soils. McGraw-Hill, New York, NY, USA.
  • Cerato A.B., Lutenegger A.J. 2004. Determining intrinsic compressibility of fine-grained soils. Journal of Geotechnical and Geoenvironmental Engineering, 130(8), 872–877.
  • Duncan J.M. 1993. Limitations of conventional analysis of consolidation settlement. Journal of Geotechnical Engineering, 119(9), 1333–1359.
  • Horn R., Lebert M. 1994. Soil compactability and compressibility. In: Developments in agricultural engineering, 11, 45–69. Elsevier.
  • Huang M.H., Zhao M.H. 2021. Semi-analytical solutions for two-dimensional plane strain consolidation of layered unsaturated soil. Computers and Geotechnics, 129, 103886.
  • Jolliffe I.T., Cadima J. 2016. Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374 (2065), 20150202.
  • Kalantary F., Kordnaeij A. 2012. Prediction of compression index using artificial neural network. Scientific Research and Essays, 7(31), 2835–2848.
  • Kumar R., Jain P.K., Dwivedi P. 2016. Prediction of compression index (Cc) of fine grained remolded soils from basic soil properties. International Journal of Applied Engineering Research, 11(1), 592–598.
  • Mandhour E.A. 2020. Prediction of Compression Index of the Soil of Al-Nasiriya City Using Simple Linear Regression Model. Geotechnical and Geological Engineering.
  • McCabe B.A., Sheil B.B., Long M.M., Buggy F.J., Farrell E.R. 2014. Empirical correlations for the compression index of Irish soft soils. Proceedings of the Institution of Civil EngineersGeotechnical Engineering, 167(6), 510–517.
  • Montgomery D.C., Peck E.A., Vining G.G. 2021. Introduction to linear regression analysis. John Wiley & Sons.
  • Nesamatha R., Arumairaj P.D. 2015. Numerical modeling for prediction of compression index from soil index properties. Electron. J. Geotech. Eng., 20, 4369–4378.
  • Nishida Y. 1956. A brief note on compression index of soil. Journal of the Soil Mechanics and Foundations Division, 82(3), 1027-1.
  • Park H.I., Lee S.R. 2011. Evaluation of the compression index of soils using an artificial neural network. Computers and Geotechnics, 38(4), 472–481.
  • Sari P.T.K., Firmansyah Y.K. 2013. The Empirical Correlation Using Linear Regression of Compression Index for Surabaya Soft Soil. In: The 2013 World Congress on Advances in Structural Engineering and Mechanics (ASEM13), 3008–3019.
  • Sivakugan N., Ameratunga J. 2021. Basic soil mechanics. In: Soft Clay Engineering and Ground Improvement. CRC Press, 31–48.
  • Sousa S.I.V., Martins F.G., Alvim-Ferraz M.C.M., Pereira M.C. 2007. Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environmental Modelling & Software, 22(1), 97–103.
  • Skempton A.W., Jones O.T. 1944. Notes on the compressibility of clays. Quarterly Journal of the Geological Society, 100(1–4), 119–135.
  • Solanki C.H., Desai M.D., Desai J.A. 2008. Statistical analysis of index and consolidation properties of alluvial deposits and new correlations. International Journal of Applied Engineering Research, 3(5), 681–689.
  • Sridharan A., Gurtug Y. 2005. Compressibility characteristics of soils. Geotechnical and Geological Engineering, 23(5), 615–634.
  • Widodo S., Ibrahim A. 2012. Estimation of primary compression index (Cc) using physical properties of Pontianak soft clay. International Journal of Engineering Research and Applications (IJERA), 2(5), 2232–2236.
  • Yamagutshi H.T.R. 1959. Characteristics of alluvial clay. Report of Kyushyu Agriculture Investigation Center of Japan, 5(4), 349–358.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9af2a98d-166b-45e1-a6e6-99e4397fd50d
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