RT info:eu-repo/semantics/article
T1 Hard-Rock Stability Analysis for Span Design in Entry-Type Excavations with Learning Classifiers
A1 García Gonzalo, Esperanza
A1 Fernández Muñiz, Zulima
A1 García Nieto, Paulino José
A1 Bernardo Sánchez, Antonio
A1 Menéndez Fernández, Marta
A2 Explotacion de Minas
K1 Ingeniería de minas
K1 Hard-rock stability
K1 Span design graph
K1 Entry-type excavations
K1 Support vector machine
K1 Extreme learning machine
K1 3318 Tecnología Minera
K1 2506.17 Mecánica de las Rocas
AB [EN] The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.
PB MDPI
LK https://hdl.handle.net/10612/17525
UL https://hdl.handle.net/10612/17525
NO García-Gonzalo, E., Fernández-Muñiz, Z., Garcia Nieto, P. J., Bernardo Sánchez, A., & Menéndez Fernández, M. (2016). Hard-rock stability analysis for span design in entry-type excavations with learning classifiers. Materials, 9(7), 531.
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 19-may-2024