TY - GEN
T1 - Stability Method for Pit Dimensioning Obtained Using the Gradient Boosting Machine Algorithm in Underground Mining
AU - Camacho, Hernan
AU - Pehovaz-Alvarez, Humberto
AU - Raymundo, Carlos
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.
PY - 2021
Y1 - 2021
N2 - The diversification of mining in different geological contexts and the need to work at higher depths has shown that the stability graph method has disregarded scenarios with the presence of water and different confinement regimes. It is for this reason that the present investigation sought to incorporate these scenarios through the Gradient Boosting Machine algorithm. For this purpose, scenarios with different levels of water pressure were simulated and the degree of confinement around the excavations was considered. The generated model was based on the binary classification criterion, I feel the predicted classes, “stable” and “unstable”; with which an AUC value of 0.88 was obtained, which demonstrated an excellent predictive capacity of the GBM model. Likewise, the advantages over the traditional method were demonstrated, since a component of rigor and generalization is added.
AB - The diversification of mining in different geological contexts and the need to work at higher depths has shown that the stability graph method has disregarded scenarios with the presence of water and different confinement regimes. It is for this reason that the present investigation sought to incorporate these scenarios through the Gradient Boosting Machine algorithm. For this purpose, scenarios with different levels of water pressure were simulated and the degree of confinement around the excavations was considered. The generated model was based on the binary classification criterion, I feel the predicted classes, “stable” and “unstable”; with which an AUC value of 0.88 was obtained, which demonstrated an excellent predictive capacity of the GBM model. Likewise, the advantages over the traditional method were demonstrated, since a component of rigor and generalization is added.
KW - Active stresses
KW - Chopping
KW - Gradient boosting machine
KW - Graphical stability method
UR - https://www.scopus.com/pages/publications/85142227496
U2 - 10.1007/978-3-030-80462-6_24
DO - 10.1007/978-3-030-80462-6_24
M3 - Contribución a la conferencia
AN - SCOPUS:85142227496
SN - 9783030804619
T3 - Lecture Notes in Networks and Systems
SP - 192
EP - 199
BT - Advances in Manufacturing, Production Management and Process Control - Proceedings of the AHFE 2021 Virtual Conferences on Human Aspects of Advanced Manufacturing, Advanced Production Management and Process Control, and Additive Manufacturing, Modeling Systems and 3D Prototyping, 2021
A2 - Trzcielinski, Stefan
A2 - Mrugalska, Beata
A2 - Karwowski, Waldemar
A2 - Rossi, Emilio
A2 - Di Nicolantonio, Massimo
PB - Springer Science and Business Media Deutschland GmbH
T2 - AHFE Conferences on Human Aspects of Advanced Manufacturing, Advanced Production Management and Process Control, and Additive Manufacturing, Modeling Systems and 3D Prototyping, 2021
Y2 - 25 July 2021 through 29 July 2021
ER -