Advertisement

Bulletin of Engineering Geology and the Environment

, Volume 78, Issue 7, pp 5039–5054 | Cite as

Cluster analysis for the determination of the undrained strength tendency from SPT in mudflows and residual soils

  • Juan C. ViviescasEmail author
  • Juan P. Osorio
  • D. V. Griffiths
Original Paper
  • 258 Downloads

Abstract

The standard penetration test (SPT) remains one of the most commonly used field tests to obtain the shear strength properties of soils, the undrained strength (cu) being one the most SPT-N-correlated parameters for geotechnical applications. The overall N-value-cu correlations show a direct relationship between them, characterized by presenting an equation formed by a constant value multiplied by N. More recently, the use of cu in geotechnical engineering has been of great interest in the evaluation of the influence of the undrained strength variability with depth on slope stability analysis. Therefore, an evaluation of the variability with depth of the N-value is made according to the geological origin. However, the (N1)60 values obtained from the SPT have different limitations due the possible “outside of tendency” data known as outliers caused by random factors such as: rock fragments content, weak zones and variations in the state of weathering, which may ultimately affect the estimation of the cu function with depth. Therefore, a cluster analysis of the SPT data was performed in order to identify the values that affect the best-fitting mathematical SPT-N function with depth. These analyses were implemented to the (N1)60 values obtained from multiple SPTs in two distinct geological units, mudflows and residual soils, in order to study the influence of the geological origin in the SPT and, therefore, of the shear strength tendency with depth. It was found that the best clustering method to identify the SPT tendency and the state of weathering in residual soils is the Ward method. For mudflows, the best cluster algorithm is the single method; however, it is concluded that for large areas, the use of a unique cluster method is not recommended. For most projects, the undrained shear strength showed a nonlinear tendency with a squared Z (where Z is the depth in meters) function being common among all geologies. The function gradient for residual soils is about twice when compared with that of the mudflows, mainly due to the overburden pressure and to the decrease in the state of weathering with depth, which increases shear strength in the former type of soils.

Keywords

Undrained strength tendency SPT Outliers Mudflows Residual soils Cluster analyses 

Notes

Acknowledgments

The authors would like to acknowledge the financial support of this research project under the National Doctoral Grant Scheme no. 727 of 2015, provided by the Administrative Department of Science, Technology and Innovation of Colombia – Colciencias.

References

  1. A.M.V.A (2006) Microzonificación sísmica detallada de los municipios de Barbosa, Girardota, Copacabana, Sabaneta, La Estrella, Caldas y Envigado. Medellín: Area Metropolitana del Valle de AburráGoogle Scholar
  2. ASTM (2011) Standard Test Method for Standard Penetration Test (SPT) and Split-Barrel Sampling of Soils. ASTM Standard Test Method D1586-11:1–9.  https://doi.org/10.1520/D1586-11.2 Google Scholar
  3. Bea R (2006) Reliability and Human Factors in Geotechnical Engineering. Journal of Geotechnical and Geoenvironmental Engineering 132(631)Google Scholar
  4. Deere DU, Patton FD (1971) Slope stability in residual soil. In Fourth Pan American Conference on Soil Mechanics and Foundation Engineering (pp. 87–170). Puerto Rico.Google Scholar
  5. Dunn JC (1973) A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics 3(3):32–57.  https://doi.org/10.1080/01969727308546046 CrossRefGoogle Scholar
  6. Everitt BS, Hothorn T (2006) A Handbook of Statistical Analyses using R. Chapman & Hall/CRC, Boca RatonGoogle Scholar
  7. Everitt BS, Hothorn T (2011) An Introduction to Applied Multivariate Analysis with R. (R. Gentleman, K. Hornik, & G. Parmigiani, Eds.), Applied Spatial Data Analysis with R. London: Springer.  https://doi.org/10.1007/978-0-387-78171-6
  8. Garcia NJ, Osorio JP (2015) Incidencia De La Gestión Del Conocimiento En La Gestión Del Riesgo En La Geotecnia (MBA Thesis). Universidad Eafit & Universidad de Antioquia. Retrieved from https://repository.eafit.edu.co/bitstream/handle/10784/7495/JuanPablo_OsorioSalas_2015.pdf?sequence=2
  9. Goktepe AB, Altun S, Sezer A (2005) Soil clustering by fuzzy c-means algorithm. Advances in Engineering Software 36(10):691–698.  https://doi.org/10.1016/j.advengsoft.2005.01.008 CrossRefGoogle Scholar
  10. Göktepe AB, Altun S, Sezer A (2015) Evaluation and use of clustering algorithms for standard penetration test data classification. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 29(1):55–64.  https://doi.org/10.1017/S089006041400033X CrossRefGoogle Scholar
  11. Griffiths DV, Fenton GA (1993) Seepage beneath water retaining structures founded on spatially random soil. Géotechnique 43(4):577–587.  https://doi.org/10.1680/geot.1993.43.4.577 CrossRefGoogle Scholar
  12. Griffiths DV, Fenton GA, Manoharan N (2002) Bearing Capacity of Rough Rigid Strip Footing on Cohesive Soil: Probabilistic Study. Journal of Geotechnical and Geoenvironmental Engineering 128(September):743–755.  https://doi.org/10.1061/(ASCE)1090-0241(2002)128:9(743) CrossRefGoogle Scholar
  13. Griffiths DV, Yu X (2015) Another look at the stability of slopes with linearly increasing undrained strength. Géotechnique 65(10):824–830.  https://doi.org/10.1680/jgeot.14.T.030 CrossRefGoogle Scholar
  14. Hamedifar H, Bea RG, Pestana-Nascimento JM, Roe EM (2014) Role of Probabilistic Methods in Sustainable Geotechnical Slope Stability Analysis. Procedia Earth and Planetary Science 9:132–142.  https://doi.org/10.1016/j.proeps.2014.06.009 CrossRefGoogle Scholar
  15. Hot E, Popović-bugarin V (2016) Soil data clustering by using K-means and fuzzy K-means algorithm. Telfor Journal 8(1):56–61CrossRefGoogle Scholar
  16. Hot E, Popović V, Topalović A, Knežević M (2016) Generating thematic pedologic maps by using data mining and interpolations. In submitted for 3nd International Conference on Electrical, Electronic and Computing Engineering IcETRAN. Zlatibor, Serbia.Google Scholar
  17. Koppula SD (1984) On Stability of Slopes in Clays with Linearly Increasing Strength. Canadian Geotechnical Journal 21(3):577–581.  https://doi.org/10.1139/t84-059 CrossRefGoogle Scholar
  18. Kulhawy F, Mayne P (1990) Manual on Estimating Soil Properties for Foundation Design. Ostigov. Retrieved from http://www.osti.gov/energycitations/product.biblio.jsp?osti_id=6653074
  19. Lacasse S, Nadim F (1998) Risk and reliability in geotechnical engineering. In Fourth International Conference On Case Histories In Geotechnical Engineering (pp. 1172–1192). St. Louis, Missouri, EE. UU.Google Scholar
  20. Little AL (1969) The engineering classification of residual tropical soils, Proceedings of the Specialty Session on the Engineering Properties of Lateritic Soils. In Seventh International Conference on Soil Mechanics and Foundation Engineering (pp. 1–10). Mexico City.Google Scholar
  21. MacQueen JB (1967) Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, Berkeley, pp 281–297Google Scholar
  22. Mitchell JK, Soga K (2005) Fundamentals of Soil Behavior. John Wiley & Sons, New JerseyGoogle Scholar
  23. Nixon J (1982) Standard penetration test: state of the art report. In Proceedings of the 2nd European Symposium on Penetration Testing (pp. 3–4). Amsterdam.Google Scholar
  24. Özvan A, Akkaya I, Tapan M (2017) An approach for determining the relationship between the parameters of pressuremeter and SPT in different consistency clays in Eastern Turkey. Bull Eng Geol Environ 77(3):1145–1154.  https://doi.org/10.1007/s10064-017-1020-9 CrossRefGoogle Scholar
  25. R Development Core Team (2008) R: A language and environment for statistical computing. R Foundation for Statistical Computing. Viena, Austria. Retrieved from www.r-project.org
  26. Rahardjo H, Satyanaga A, Leong EC, Ng YS, Pang HTC (2012) Variability of residual soil properties. Engineering Geology 141–142:124–140.  https://doi.org/10.1016/j.enggeo.2012.05.009 CrossRefGoogle Scholar
  27. Rencher A (2002) Methods of multivariate analysis, vol 59, 2nd edn. John Wiley & Sons, New YorkCrossRefGoogle Scholar
  28. Rendón RA, Caballero AJ, Arias LA, González PA, Arenas RJ, Gallego J (2011) Estudio geológico-geomorfológico en el oriente cercano a Medellín, como apoyo a la búsqueda de actividad tectónica reciente. Boletín de Ciencias de La Tierra (29):39–54Google Scholar
  29. Restrepo JJ, Toussaint J (1984) Unidades litolóficas de los alrededores de Medellin. In I conf. de riesgos geologicos del Valle de Aburrá (p. 26). Medellin.Google Scholar
  30. Rokach L, Maimon O (2010) Clustering methods. In Data Mining and Knowledge Discovery Handbook (2nd ed., pp. 321–352). Springer US.  https://doi.org/10.1007/0-387-25465-X_15
  31. Sanglerat G (1972) The Penetrometer and Soil Exploration; Interpretation of Penetration Diagrams. Elsevier Publishing Co., AmsterdamGoogle Scholar
  32. Sivrikaya O, Toğrol E (2002) Relations between SPT-N and qu. In 5th International Congress on Advances in Civil Engineering (pp. 943–952). Instanbul, Turkey.Google Scholar
  33. Sivrikaya O, Toğrol E (2006) Determination of undrained strength of fine-grained soils by means of SPT and its application in Turkey. Engineering Geology 86(1):52–69.  https://doi.org/10.1016/j.enggeo.2006.05.002 CrossRefGoogle Scholar
  34. Sowers G (1979) Introductory Soil Mechanics and Foundations. (Macmillan, Ed.) (4th edition). New York.Google Scholar
  35. Terzaghi K, Peck R (1967) Soil Mechanics in Engineering Practice. John Wiley & Sons, New YorkGoogle Scholar
  36. Tuncer RE, Lohnes RA (1977) An engineering classification of certain basalt-derived lateritic soils. Engineering Geology 11(4):319–339CrossRefGoogle Scholar
  37. Van Staveren MT (2006) Uncertainty and Ground Conditions: A Risk Management Apprach. (Elsevier, Ed.). Oxford.Google Scholar
  38. Vinet L, Zhedanov A (2010) A “missing” family of classical orthogonal polynomials. Antimicrobial Agents and Chemotherapy 58(12):7250–7257.  https://doi.org/10.1088/1751-8113/44/8/085201 Google Scholar
  39. Viviescas JC, Osorio JP, Cañón JE (2017) Reliability-based designs procedure of earth retaining walls in geotechnical engineering. Obras y Proyectos (22):50–60.  https://doi.org/10.4067/S0718-28132017000200050
  40. Ward JH (1963) Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association 58:236–244CrossRefGoogle Scholar
  41. Wicander R, Monroe JS (1999) Essentials of Physical Geology (second edition). Wadsworth Publishing CompanyGoogle Scholar
  42. Zhao HF, Zhang LM (2014) Instability of Saturated and Unsaturated Coarse Granular Soils. J. Geotech. Geoenviron. Eng. 140(1):25–35.  https://doi.org/10.1061/(ASCE)GT.1943-5606.0000976 CrossRefGoogle Scholar
  43. Zhao HF, Zhang LM, Xu Y, Chang DS (2013) Variability of geotechnical properties of a fresh landslide soil deposit. Engineering Geology 166:1–10.  https://doi.org/10.1016/j.enggeo.2013.08.006 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.GeoResearch International – GeoR, Escuela Ambiental, Facultad de IngenieríaUniversidad de Antioquia UdeAMedellínColombia
  2. 2.School of Civil and Structural EngineeringTechnological University DublinDublin 1Ireland
  3. 3.Department of Civil and Environmental Engineering, Colorado School of MinesGoldenUSA

Personalised recommendations