Automatic determination of the Atterberg limits with machine learning

• David Antonio Rosas ^[1] ; Daniel Burgos ^[1] ; Jhon Willian Branch Bedoya ^[2] ; Alberto Corbí ^[1]
  1. [1] Universidad Internacional de La Rioja

     Universidad Internacional de La Rioja

     Logroño, España

     
  2. [2] Universidad Nacional de Colombia

     Universidad Nacional de Colombia

     Colombia

     
• Localización: DYNA: revista de la Facultad de Minas. Universidad Nacional de Colombia. Sede Medellín, ISSN 0012-7353, Vol. 89, Nº. 224, 2022, págs. 34-42
• Idioma: inglés
• DOI: 10.15446/dyna.v89n224.102619
• Títulos paralelos:
  • Determinación automática de los límites de Atterberg con machine learning
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    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.

  • English

    In this study, we determine the liquid limit (????), plasticity index (PI), and plastic limit (????) of several natural fine-grained soil sampleswith the help of machine-learning and statistical methods. This enables us to locate each soil type analysed in the Casagrande plasticitychart 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, ????=(9. 94± 4.2)+(2. 25 ± 0.3)∙????4.2,PI=(−20.47± 5.6)+(1. 48 ± 0.3)∙????4.2+(0. 21± 0.1)∙??, and ????=(23.32± 3.5)+(0. 60 ± 0.2)∙????4.2−(0. 13± 0.04)∙??. So that, we propose an alternative, automatic, multi-sample, and static method to address current issues on Atterberg limitsdetermination with standardised tests.

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