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Generation of penetrometric profile of the soil applying machine learning to measure while drilling data from deep foundation machinery
Cita
Martínez García, Eduardo 
ORCID: https://orcid.org/0000-0003-2814-2644, García Alberti, Marcos ORCID: https://orcid.org/0000-0002-7276-8030 and Arcos Álvarez, Antonio Alfonso ORCID: https://orcid.org/0000-0002-7189-5871
(2025).
Generation of penetrometric profile of the soil applying machine learning to measure while drilling data from deep foundation machinery.
"Applied Sciences", v. 15
(n. 3);
p. 1331.
ISSN 2076-3417.
https://doi.org/10.3390/app15031331.
ORCID: https://orcid.org/0000-0003-2814-2644, García Alberti, Marcos ORCID: https://orcid.org/0000-0002-7276-8030 and Arcos Álvarez, Antonio Alfonso ORCID: https://orcid.org/0000-0002-7189-5871
(2025).
Generation of penetrometric profile of the soil applying machine learning to measure while drilling data from deep foundation machinery.
"Applied Sciences", v. 15
(n. 3);
p. 1331.
ISSN 2076-3417.
https://doi.org/10.3390/app15031331.
Descripción
| Título: | Generation of penetrometric profile of the soil applying machine learning to measure while drilling data from deep foundation machinery |
|---|---|
| Autor/es: |
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| Tipo de Documento: | Artículo |
| Título de Revista/Publicación: | Applied Sciences |
| Fecha: | 27 Enero 2025 |
| ISSN: | 2076-3417 |
| Volumen: | 15 |
| Número: | 3 |
| Materias: | |
| ODS: | |
| Palabras Clave Informales: | Machine learning; rigid inclusion; penetrometer; measurement while drilling; dynamic time warping |
| Escuela: | E.T.S.I. Caminos, Canales y Puertos (UPM) |
| Departamento: | Ingeniería Civil: Construcción |
| Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
Resumen
The study performed in this article aimed to reproduce the penetrometric profile of the soil from the perforation parameters of deep foundation and ground improvement. This could allow for more easily interpretable information on the soil strength during execution as well as validate the design hypotheses. To achieve this goal, a series of Machine Learning algorithms have been used and compared with traditionally applied analytical formulas. Dynamic time warping is used to measure the likeness of the results with the expected shape. The results show that the algorithms are capable of better fitting the penetrometric profiles of the soil. Tree ensemble methods stand out with the best results.
Más información
| ID de Registro: | 92081 |
|---|---|
| Identificador DC: | https://oa.upm.es/92081/ |
| Identificador OAI: | oai:oa.upm.es:92081 |
| URL Portal Científico: | https://portalcientifico.upm.es/es/ipublic/item/10385524 |
| Identificador DOI: | 10.3390/app15031331 |
| URL Oficial: | https://www.mdpi.com/2076-3417/15/3/1331 |
| Depositado por: | iMarina Portal Científico |
| Depositado el: | 02 Dic 2025 11:49 |
| Ultima Modificación: | 02 Dic 2025 11:56 |
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