Determination of the California Bearing Ratio of the Subgrade and Granular Base Using Artificial Neural Networks
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 mecha...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taiwan Association of Engineering and Technology Innovation
2023-05-01
|
| Series: | International Journal of Engineering and Technology Innovation |
| Subjects: | |
| Online Access: | https://ojs.imeti.org/index.php/IJETI/article/view/11053 |
| _version_ | 1797807530291757056 |
|---|---|
| author | Jose Manuel Palomino Ojeda Billy Alexis Cayatopa Calderon Lenin Quiñones Huatangari Wilmer Rojas Pintado |
| author_facet | Jose Manuel Palomino Ojeda Billy Alexis Cayatopa Calderon Lenin Quiñones Huatangari Wilmer Rojas Pintado |
| author_sort | Jose Manuel Palomino Ojeda |
| collection | DOAJ |
| description |
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.
|
| first_indexed | 2024-03-13T06:23:56Z |
| format | Article |
| id | doaj.art-941dcb4ba2254900822f1be2051967b3 |
| institution | Directory Open Access Journal |
| issn | 2223-5329 2226-809X |
| language | English |
| last_indexed | 2024-03-13T06:23:56Z |
| publishDate | 2023-05-01 |
| publisher | Taiwan Association of Engineering and Technology Innovation |
| record_format | Article |
| series | International Journal of Engineering and Technology Innovation |
| spelling | doaj.art-941dcb4ba2254900822f1be2051967b32023-06-09T11:04:53ZengTaiwan Association of Engineering and Technology InnovationInternational Journal of Engineering and Technology Innovation2223-53292226-809X2023-05-0110.46604/ijeti.2023.11053Determination of the California Bearing Ratio of the Subgrade and Granular Base Using Artificial Neural NetworksJose Manuel Palomino Ojeda0Billy Alexis Cayatopa Calderon1Lenin Quiñones Huatangari2Wilmer Rojas Pintado3Instituto de Ciencia de Datos, Universidad Nacional de Jaen, Jaen, PeruInstituto de Investigación en Sismológica y Construcción, Universidad Nacional de Jaen, Jaen, PeruInstituto de Ciencia de Datos, Universidad Nacional de Jaen, Jaen, PeruInstituto de Investigación en Sismológica y Construcción, Universidad Nacional de Jaen, Jaen, Peru 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. https://ojs.imeti.org/index.php/IJETI/article/view/11053CBRsubgradesoilpredictionmodel |
| spellingShingle | Jose Manuel Palomino Ojeda Billy Alexis Cayatopa Calderon Lenin Quiñones Huatangari Wilmer Rojas Pintado Determination of the California Bearing Ratio of the Subgrade and Granular Base Using Artificial Neural Networks International Journal of Engineering and Technology Innovation CBR subgrade soil prediction model |
| title | Determination of the California Bearing Ratio of the Subgrade and Granular Base Using Artificial Neural Networks |
| title_full | Determination of the California Bearing Ratio of the Subgrade and Granular Base Using Artificial Neural Networks |
| title_fullStr | Determination of the California Bearing Ratio of the Subgrade and Granular Base Using Artificial Neural Networks |
| title_full_unstemmed | Determination of the California Bearing Ratio of the Subgrade and Granular Base Using Artificial Neural Networks |
| title_short | Determination of the California Bearing Ratio of the Subgrade and Granular Base Using Artificial Neural Networks |
| title_sort | determination of the california bearing ratio of the subgrade and granular base using artificial neural networks |
| topic | CBR subgrade soil prediction model |
| url | https://ojs.imeti.org/index.php/IJETI/article/view/11053 |
| work_keys_str_mv | AT josemanuelpalominoojeda determinationofthecaliforniabearingratioofthesubgradeandgranularbaseusingartificialneuralnetworks AT billyalexiscayatopacalderon determinationofthecaliforniabearingratioofthesubgradeandgranularbaseusingartificialneuralnetworks AT leninquinoneshuatangari determinationofthecaliforniabearingratioofthesubgradeandgranularbaseusingartificialneuralnetworks AT wilmerrojaspintado determinationofthecaliforniabearingratioofthesubgradeandgranularbaseusingartificialneuralnetworks |