Communication Dans Un Congrès Année : 2025

Using Machine Learning for Predicting Collapse extending in Abandoned Underground Mines

Résumé

The main objective of this work is to explore the advantages of applying machine learning tools to the analysis of collapse related to abandoned underground mines. To this end, the present work focuses on the analysis of data recorded by Ineris for 143 collapses cases, with particular attention to the extension of the collapse to the surface. This work presents the procedure followed from the creation of a clean database, through the selection of features, to the proposal of a tool capable of estimating the extension of the collapse to the surface. This exploratory work confirms the significant potential of using machine learning on geotechnical data.

Fichier principal
Vignette du fichier
2025-090 post-print.pdf (520.96 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-05312019 , version 1 (13-10-2025)

Licence

Identifiants

  • HAL Id : hal-05312019 , version 1

Citer

Lina-María Guayacán-Carrillo, Nathalie Conil, A. Kadri. Using Machine Learning for Predicting Collapse extending in Abandoned Underground Mines. ISRM European Rock Mechanics Symposium (EUROCK 2025), Jun 2025, Trondheim, Norway. ⟨hal-05312019⟩
61 Consultations
141 Téléchargements

Partager

  • More