Determination of the California Bearing Ratio of the Subgrade and Granular Base Using Artificial Neural Networks

Jose Manuel Palomino Ojeda; Billy Alexis Cayatopa Calderon; Lenin Quiñones Huatangari; Wilmer Rojas Pintado

doi:10.46604/ijeti.2023.11053

Authors

Jose Manuel Palomino Ojeda Instituto de Ciencia de Datos, Universidad Nacional de Jaen, Jaen, Peru
Billy Alexis Cayatopa Calderon Instituto de Investigación en Sismológica y Construcción, Universidad Nacional de Jaen, Jaen, Peru
Lenin Quiñones Huatangari Instituto de Ciencia de Datos, Universidad Nacional de Jaen, Jaen, Peru
Wilmer Rojas Pintado Instituto de Investigación en Sismológica y Construcción, Universidad Nacional de Jaen, Jaen, Peru

DOI:

https://doi.org/10.46604/ijeti.2023.11053

Keywords:

CBR, subgrade, soil, prediction, model

Abstract

The objective of the research is to estimate the value of the California bearing ratio (CBR) through the application of ANN. The methodology consists of creating a database with soil index and CBR variables of the subgrades and granular base of pavements in Jaen, Peru, carried out in the soil mechanics laboratories of the city and the National University of Jaen. In addition, the Python library Seaborn is for variable selection and relevance, and the scikit-learn and Keras libraries were used for the learning, training, and validation stage. Five ANN are proposed to estimate the CBR value, obtaining an error of 4.47% in the validation stage. It can be concluded that this method is effective and valid to determine the CBR value in subgrades and granular bases of any pavement for its evaluation or design.

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