Automatic determination of the Atterberg limits with machine learning

  1. David Antonio Rosas 1
  2. Daniel Burgos 1
  3. Jhon Willian Branch Bedoya 2
  4. Alberto Corbí 1
  1. 1 Research Institute for Innovation & Technology in Education (UNIR iTED), Universidad Internacional de La Rioja (UNIR), Logroño, La Rioja, Spain.
  2. 2 Universidad Nacional de Colombia, Sede Medellín, Facultad de Minas, Departamento de Ciencias de la Computación y de la Decisión, Medellín.
Revista:
DYNA: revista de la Facultad de Minas. Universidad Nacional de Colombia. Sede Medellín

ISSN: 0012-7353

Año de publicación: 2022

Volumen: 89

Número: 224

Páginas: 34-42

Tipo: Artículo

DOI: 10.15446/DYNA.V89N224.102619 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: DYNA: revista de la Facultad de Minas. Universidad Nacional de Colombia. Sede Medellín

Objetivos de desarrollo sostenible

Resumen

En este estudio, determinamos el límite líquido (����), el índice de plasticidad (PI) y el límite plástico (����) de suelos naturales finos con ayuda de machine-learning y métodos estadísticos. Ello permite localizarlos en la Carta de Plasticidad de Casagrande con una sola medida en extractores de presión-membrana. Los modelos de machine-learning mostraron ajustes en la determinación de ���� apropiados para propósitos de diseño, comparados con métodos estandarizados. Ajustes similares se alcanzaron en la determinación de PI, mientras que las determinaciones de ���� permiten ajustes apropiados para trabajos de control. Debido a que las técnicas más apropiadas se basaron en Regresión Lineal Múltiple y Máquinas de Soporte de Vectores, aportaron modelos de plasticidad explicables. En este sentido, ����=(9. 94± 4.2)+(2. 25 ± 0.3)∙����4.2,����=(−20.47± 5.6)+(1. 48 ± 0.3)∙����4.2+(0. 21± 0.1)∙��y����=(23.32± 3.5)+(0. 60 ± 0.2)∙����4.2−(0. 13± 0.04)∙��. Por consiguiente, proponemos un método alternativo, automático, estático y multimuestra para enfrentar problemas frecuentes en la determinación de los Límites de Atterberg con ensayos normalizados.

Referencias bibliográficas

  • Blackall, T.E., A.M. Atterberg 1846-1916. Geotechnique, 3(1), pp.17-19,1952. DOI: https://doi.org/10.1680/geot.1952.3.1.17
  • Galindo, R., Lara, A. and Guillán, G., Contribution to the knowledge of early geotechnics during the twentieth century: Arthur Casagrande. History of Geo- and Space Sciences, 9(2), pp.107-123, 2018. DOI: https://doi.org/10.5194/hgss-9-107-2018
  • Casagrande, A., Classification and identification of soils. In: Proceedings of the American Society of Civil Engineers, [online]. 1947, pp. 901-991. [date of reference May 11th of 2022]. Available at: https://cedb.asce.org/CEDBsearch/record.jsp?dockey=0371060
  • Normas NLT I: ensayos de carreteras. Granulometría de suelos por tamizado (NLT 104/91). Madrid: Centro de Estudios y Experimentación de Obras Públicas. 1992.
  • American Society for Testing and Materials. Standard practice for classification of soils for engineering purposes: unified soil classification system (ASTM D 2487-06). ASTM International, West Conshohocken, PA, USA,. 2006. DOI: https://doi.org/10.1520/D2487-06
  • American Society for Testing and Materials. Standard practice for classification of soils and soil-aggregate mixtures for highway construction purposes (ASTM D 3282-93). ASTM International, West Conshohocken, PA, USA, 2004. DOI: https://doi.org/1010.1520/D3282-93R04E01
  • Normas NLT I: ensayos de carreteras. Determinación del límite líquido de un suelo por el método de Casagrande (NLT 105/91). Centro de Estudios y Experimentación de Obras Públicas, Madrid, España, 1992.
  • Wires, K.C., The Casagrande method versus the drop-cone penetrometer method for the determination of liquid limit. Canadian Journal of Soil Science, 64(2), pp. 297-300,1 984. DOI: https://doi.org/10.4141/cjss84-031
  • Rosas, D.A., Procedimiento para la determinación del límite líquido, limite plástico e índice de plasticidad mediante extractor de presión membrana o ‘aparato de Richards’. Patent ES2301297A1. [online]. 2005 [date of reference May 11th of 2022]. Available at: https://patents.google.com/patent/ES2301297A1/es?oq=david+antonio+rosas+espin
  • Richards, L.A., A pressure-membrane extraction apparatus for soil solution. Soil Science, 51(5), pp. 377-386, 1941. DOI: https://doi.org/10.1097/00010694-194105000-00005
  • Sherwood, P.T., The reproducibility of the results of soil classification and compaction tests. RRL Reports, Road Research Lab/UK/. [online]. 1970. [date of reference May 11th of 2022]. Available at: https://trid.trb.org/view/121268
  • Torres, A. and Tadeo, A.I., Análisis de la Norma de Ensayo NLT 105/91, ‘Determinación del límite líquido de un suelo por el método del aparato Casagrande’. Revista Digital del Cedex (117), pp. 93-93, [online]. 2000. [date of reference May 11th of 2022]. Available at: http://ingenieriacivil.cedex.es/index.php/ingenieria-civil/article/view/1535
  • Haigh, S.K., Vardanega, P.J. and Bolton, M.D., The plastic limit of clays. Géotechnique, 63(6), pp. 435-440, 2013. DOI: https://doi.org/10.1680/geot.11.P.123
  • Sorensen, K.K. and Okkels, N., Correlation between drained shear strength and plasticity index of undisturbed overconsolidated clays. In: Proceedings of the 18th International Conference on Soil Mechanics and Geotechnical Engineering, [online]. 1, pp. 423-428, 2013. [date of reference May 11th of 2022]. Available at: https://www.geo.dk/media/1209/correlation-between-drained-shear-strength-and-plasticity-index-of-undisturbed-overconsolidated-clays_final-after-1-review_b_sorensen-okkels.pdf
  • Soil Survey Staff., Soil survey field and laboratory methods manual. Soil Survey Investigations Report No. 51, Version 2.0. R. Burt and Soil Survey Staff (ed.). U.S. Department of Agriculture, Natural Resources Conservation Service. [online]. 2014.[date of reference May 11th of 2022]. Available at: https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb1244466.pdf
  • Shinseki, E.K., and Hudson, J.B., The unified soil classification system. Material Testing Field Manual No. 5-742. Appendix B. Headquarters of the Department of the Army. [online]. Washington,
  • D.C., USA, 2001. Available at: http://www.globalsecurity.org/military/library/policy/army/fm/5-472/fm5-472reprint.pdf
  • Schmitz, R.M., Schroeder, C. and Charlier, R., Chemo-mechanical interactions in clay: a correlation between clay mineralogy and Atterberg limits. Applied Clay Science, 26(1-4), pp. 351-358, 2014. DOI: https://doi.org/10.1016/j.clay.2003.12.015
  • Lambe, T.W. and Whitman, R.V., Soil mechanics. John Wiley & Sons, New York, USA, 1969.
  • American Society for Testing and Materials. Standard test method for liquid limit, plastic limit, and plasticity index of soils (D4318-17e1). ASTM International. West Conshohocken, PA, USA, 2018. doi: 10.1520/D4318-17E01
  • Sanz, C., Galindo, J., Alfaro, P., and Ruano, P. El relieve de la Cordillera Bética. Enseñanza de las Ciencias de la Tierra, 15(2), pp.185-195, 2007. [date of reference May 11th of 2022]. Available at: https://www.raco.cat/index.php/ECT/article/download/120969/166484
  • Zhang, C. and Lu, N., Unitary definition of matric suction. Journal of Geotechnical and Geoenvironmental Engineering, 145(2), art. 02818004, [online]. 2019. [date of reference May 11th of 2022]. Available at: https://doi.org/10.1061/(ASCE)GT.1943-5606.0002004
  • Cianfrani, C., Buri, A., Vittoz, P., Grand, S., Zingg, B., Verrecchia, E. and Guisan, A., Spatial modelling of soil water holding capacity improves models of plant distributions in mountain landscapes. Plant and Soil, 438(1-2), pp.57-70, 2019. DOI: https://doi.org/10.1007/s11104-019-04016-x
  • Torrent, J., Campillo, M.C. and Barrón, V., Predicting cation exchange capacity from hygroscopic moisture in agricultural soils of Western Europe. Spanish Journal of Agricultural Research, 13(4), art. 8212, 2015. DOI: http://dx.doi.org/10.5424/sjar/2015134-8212
  • Wuddivira, M.N., Robinson, D.A., Lebron, I., Bréchet, L., Atwell, M., De Caires, S. and Tuller, M., Estimation of soil clay content from hygroscopic water content measurements. Soil Science Society of America Journal, 76(5), pp. 1529-1535, 2012. Doi: https://doi.org/10.2136/sssaj2012.0034
  • Brunauer, S., Emmett, P.H. and Teller, E., Adsorption of gases in multimolecular layers. Journal of the American Chemical Society, 60(2), pp. 309-319, 1938. DOI: https://doi.org/10.1021/ja01269a023
  • Lu, N. and Zhang, C., Soil sorptive potential: concept, theory, and verification. Journal of Geotechnical and Geoenvironmental Engineering, [online]. 145(4), art. 04019006, 2019. [date of reference May 11th of 2022]. Available at: https://doi.org/10.1061/(ASCE)GT.1943-5606.0002025
  • Lu, N., Generalized soil water retention equation for adsorption and capillarity. Journal of Geotechnical and Geoenvironmental Engineering, [online]. 142(10), art. 04016051, 2016. [date of reference May 11th of 2022]. Available at: https://doi.org/10.1061/(ASCE)GT.1943-5606.0001524
  • Pham, H.Q., Fredlund, D.G. and Barbour, S.L., A study of hysteresis models for soil-water characteristic curves. Canadian Geotechnical Journal, 42(6), pp. 1548-1568, 2005. DOI: https://doi.org/10.1139/t05-071
  • Weil, R.R. and Brady, N.C., The nature and properties of soils: Global edition. England: Pearson, 2016.
  • Kim, W.S. and Borden, R.H., Influence of soil type and stress state on predicting shear strength of unsaturated soils using the soil-water characteristic curve. Canadian Geotechnical Journal, 48(12), pp. 1886-1900, 2011. DOI: https://doi.org/10.1139/t11-082
  • IBM corp. SPSS V.25 documentation. [date of reference May 11th of 2022]. Available at: https://www.ibm.com/mysupport/s/topic/0TO500000001yjtGAA/spss-statistics?language=en_US
  • Chow, C.K., Statistical independence and threshold functions. IEEE Transactions on Electronic Computers, EC-14(1), pp.66-68, 1965. DOI: https://doi.org/10.1109/pgec.1965.264059
  • Woźniak, M., Graña, M. and Corchado, E., A survey of multiple classifier systems as hybrid systems. Information Fusion, 16, pp.3-17, 2014. DOI: https://doi.org/10.1016/j.inffus.2013.04.006
  • Van Rossum, G. and Drake, F.L., Python 3 Reference Manual. CreateSpace, Scotts Valley, CA, USA, 2021. [date of reference May 11th of 2022]. Available at: https://docs.python.org/3/reference/
  • McKinney, W., Data structures for statistical computing in Python, In: Proceedings of the 9th Python in Science Conference, vol. 445, 2011, pp. 51-56. DOI: https://doi.org/10.25080/majora-92bf1922-00a
  • Waskom, M., Botvinnik, O., O’Kane, D., Hobson, P., Lukauskas, S., Gemperline, D. and Qalieh, A., Mwaskom/Seaborn: v0.8.1. Zenodo. September, 2017. [date of reference May 11th of 2022] DOI: https://doi.org/10.5281/zenodo.883859
  • Hunter, J.D., Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), pp. 90-95, 2017. DOI: : https://doi.org/10.1109/mcse.2007.55
  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O. and Vanderplas, J., Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, pp. 2825-2830, 2011. DOI: https://dl.acm.org/doi/10.5555/1953048.2078195
  • Seabold, S. and Perktold, J., Statsmodels: econometric and statistical modeling with python, in: Proceedings of the 9th Python in Science Conference, vol. 57, 61 P., 2010. DOI: https://doi.org/10.25080/majora-92bf1922-011
  • Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B.E., Bussonnier, M., Frederic, J. and Ivanov, P., Jupyter Notebooks: a publishing format for reproducible computational workflows IOS Press Ebooks, 2016, pp. 87-90. [date of reference May 11th of 2022]. DOI: https://doi.org/10.3233/978-1-61499-649-1-87
  • Scikit-learn Developers. Linear models (v. 0.23.2). [online]. 2020. [date of reference May 11th of 2022]. Available at: https://scikit-learn.org/stable/modules/linear_model.html
  • Breiman, L., Random forests. Machine learning, 45(1), pp. 5-32, 2011. DOI: https://doi.org/10.1023/A:1010933404324
  • Smola, A.J. and Schölkopf, B., A tutorial on support vector regression. Statistics and Computing, 14(3), pp. 199-222, 2014. DOI: https://doi.org/10.1023/B:STCO.0000035301.49549.88
  • Kozak, A. and Kozak, R., Does cross validation provide additional information in the evaluation of regression models?, Canadian Journal of Forest Research, 33(6), pp. 976-987, 2003. DOI: https://doi.org/10.1139/x03-022
  • Stone, M., Cross‐validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), pp. 111-133, 1974. DOI: https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
  • Kohavi, R., A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai, 14(2), pp. 1137-1145, 1995. DOI: https://dl.acm.org/doi/10.5555/1643031.1643047
  • Shimobe, S. and Spagnoli, G., A global database considering Atterberg limits with the Casagrande and fall-cone tests. Engineering Geology, 260, art. 105201, 2019. DOI: https://doi.org/10.1016/j.enggeo.2019.105201
Fuente de los datos: Dialnet