TY - GEN
T1 - Método de Predicción de Estallido de Roca en Minería Subterránea de Gran Profundidad basado en Extreme Learning Machine
AU - Pastor-Villanueva, Sebastian
AU - Pehovaz-Alvarez, Humberto
AU - Raymundo, Carlos
N1 - Publisher Copyright:
© 2021 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
PY - 2021
Y1 - 2021
N2 - In great depth underground mining the stress accumulation in the rockmass leads to a condition known as Rockburst. Until now, no detection method has proven to be successful enough in detecting rockburst events. Because of that, a software has been developed in order to predict the probability of rockburst using as data entry the in-situ stress condition and geomechanics properties of the rockmass. This software is based on Extreme Learning Machine, a single perceptron feedforward Neuronal Network that uses random projection. The foreseen result is a detection of 90% of cases and an 85% of effectivity of rockburst quality prediction. The database has Acoustic Emission readings of different great depth mines around the globe.
AB - In great depth underground mining the stress accumulation in the rockmass leads to a condition known as Rockburst. Until now, no detection method has proven to be successful enough in detecting rockburst events. Because of that, a software has been developed in order to predict the probability of rockburst using as data entry the in-situ stress condition and geomechanics properties of the rockmass. This software is based on Extreme Learning Machine, a single perceptron feedforward Neuronal Network that uses random projection. The foreseen result is a detection of 90% of cases and an 85% of effectivity of rockburst quality prediction. The database has Acoustic Emission readings of different great depth mines around the globe.
KW - Deep underground mining
KW - Extreme learning machine
KW - Neural network
KW - Rockburst
UR - https://www.scopus.com/pages/publications/85121999291
U2 - 10.18687/LACCEI2021.1.1.503
DO - 10.18687/LACCEI2021.1.1.503
M3 - Contribución a la conferencia
AN - SCOPUS:85121999291
T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
BT - 19th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
A2 - Larrondo Petrie, Maria M.
A2 - Zapata Rivera, Luis Felipe
A2 - Aranzazu-Suescun, Catalina
PB - Latin American and Caribbean Consortium of Engineering Institutions
T2 - 19th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: "Prospective and Trends in Technology and Skills for Sustainable Social Development" and "Leveraging Emerging Technologies to Construct the Future", LACCEI 2021
Y2 - 19 July 2021 through 23 July 2021
ER -