Publicado

2022-11-16

Automatic determination of the Atterberg limits with machine learning•

Determinación automática de los límites de Atterberg con machine learning

DOI:

https://doi.org/10.15446/dyna.v89n224.102619

Palabras clave:

machine learning; Atterberg limits; pressure-membrane extractor; determination; soils (en)
machine learning; límites de Atterberg; extractor de presión membrana; determinación; suelo (es)

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Autores/as

  • David Antonio Rosas Research Institute for Innovation & Technology in Education (UNIR iTED), Universidad Internacional de La Rioja (UNIR), Logroño, La Rioja, Spain https://orcid.org/0000-0002-9722-2659
  • Daniel Burgos Research Institute for Innovation & Technology in Education (UNIR iTED), Universidad Internacional de La Rioja (UNIR), Logroño, La Rioja, Spain https://orcid.org/0000-0003-0498-1101
  • John Willian Branch Bedoya Universidad Nacional de Colombia, Sede Medellín, Facultad de Minas, Departamento de Ciencias de la Computación y de la Decisión, Medellín, Colombia https://orcid.org/0000-0002-0378-028X
  • Alberto Corbi Research Institute for Innovation & Technology in Education (UNIR iTED), Universidad Internacional de La Rioja (UNIR), Logroño, La Rioja, Spain https://orcid.org/0000-0002-7282-4557

In this study, we determine the liquid limit (𝑊𝑙), plasticity index (PI), and plastic limit (𝑊𝑝) of several natural fine-grained soil samples with the help of machine-learning and statistical methods. This enables us to locate each soil type analysed in the Casagrande plasticity chart with a single measure in pressure-membrane extractors. These machine-learning models showed adjustments in the determination of the liquid limit for design purposes when compared with standardised methods. Similar adjustments were achieved in the determination of the plasticity index, whereas the plastic limit determinations were applicable for control works. Because the best techniques were based in Multiple Linear Regression and Support Vector Machines Regression, they provide explainable plasticity models. In this sense, 𝑊𝑙=(9.94±4.2)+(2.25 ±0.3)∙𝑝F4.2, PI=(−20.47±5.6)+(1.48 ±0.3)∙𝑝F4.2+(0.21±0.1)∙𝐹 , and 𝑊𝑝=(23.32±3.5)+(0.60 ±0.2)∙𝑝F4.2−(0.13±0.04)∙𝐹 . So that, we propose an alternative, automatic, multi-sample, and static method to address current issues on Atterberg limits determination with standardised tests.

En este estudio, determinamos el límite líquido (𝑊𝑙), el índice de plasticidad (PI) y el límite plástico (𝑊𝑝) 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 𝑊𝑊𝑙𝑙 apropiados para propósitos de diseño, comparados con métodos estandarizados. Ajustes similares se alcanzaron en la determinación de PI, mientras que las determinaciones de 𝑊𝑊𝑝𝑝 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, 𝑊𝑙=(9.94±4.2)+(2.25 ±0.3)∙𝑝F4.2, 𝑃I=(−20.47±5.6)+(1.48 ±0.3)∙𝑝F4.2+(0.21±0.1)∙𝐹 y 𝑊𝑝=(23.32±3.5)+(0.60 ±0.2)∙𝑝F4.2−(0.13±0.04)∙𝐹 . 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.

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Cómo citar

IEEE

[1]
D. A. . Rosas, D. Burgos, J. W. Branch Bedoya, y A. Corbi, «Automatic determination of the Atterberg limits with machine learning•», DYNA, vol. 89, n.º 224, pp. 34–42, nov. 2022.

ACM

[1]
Rosas, D.A. , Burgos , D., Branch Bedoya, J.W. y Corbi, A. 2022. Automatic determination of the Atterberg limits with machine learning•. DYNA. 89, 224 (nov. 2022), 34–42. DOI:https://doi.org/10.15446/dyna.v89n224.102619.

ACS

(1)
Rosas, D. A. .; Burgos , D.; Branch Bedoya, J. W.; Corbi, A. Automatic determination of the Atterberg limits with machine learning•. DYNA 2022, 89, 34-42.

APA

Rosas, D. A. ., Burgos , D., Branch Bedoya, J. W. & Corbi, A. (2022). Automatic determination of the Atterberg limits with machine learning•. DYNA, 89(224), 34–42. https://doi.org/10.15446/dyna.v89n224.102619

ABNT

ROSAS, D. A. .; BURGOS , D.; BRANCH BEDOYA, J. W.; CORBI, A. Automatic determination of the Atterberg limits with machine learning•. DYNA, [S. l.], v. 89, n. 224, p. 34–42, 2022. DOI: 10.15446/dyna.v89n224.102619. Disponível em: https://revistas.unal.edu.co/index.php/dyna/article/view/102619. Acesso em: 14 mar. 2026.

Chicago

Rosas, David Antonio, Daniel Burgos, John Willian Branch Bedoya, y Alberto Corbi. 2022. «Automatic determination of the Atterberg limits with machine learning•». DYNA 89 (224):34-42. https://doi.org/10.15446/dyna.v89n224.102619.

Harvard

Rosas, D. A. ., Burgos , D., Branch Bedoya, J. W. y Corbi, A. (2022) «Automatic determination of the Atterberg limits with machine learning•», DYNA, 89(224), pp. 34–42. doi: 10.15446/dyna.v89n224.102619.

MLA

Rosas, D. A. ., D. Burgos, J. W. Branch Bedoya, y A. Corbi. «Automatic determination of the Atterberg limits with machine learning•». DYNA, vol. 89, n.º 224, noviembre de 2022, pp. 34-42, doi:10.15446/dyna.v89n224.102619.

Turabian

Rosas, David Antonio, Daniel Burgos, John Willian Branch Bedoya, y Alberto Corbi. «Automatic determination of the Atterberg limits with machine learning•». DYNA 89, no. 224 (noviembre 15, 2022): 34–42. Accedido marzo 14, 2026. https://revistas.unal.edu.co/index.php/dyna/article/view/102619.

Vancouver

1.
Rosas DA, Burgos D, Branch Bedoya JW, Corbi A. Automatic determination of the Atterberg limits with machine learning•. DYNA [Internet]. 15 de noviembre de 2022 [citado 14 de marzo de 2026];89(224):34-42. Disponible en: https://revistas.unal.edu.co/index.php/dyna/article/view/102619

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