ABSTRACT
Collapses have been a major geomechanical issue at El Teniente Mine, due to its difficult treatment and the known fact that actually there are neither methodologies nor tools to estimate their possible occurrences or successfully holding their progression. The term “collapse” is understood as a physical process consisting in major large-scale deformations whose later manifestation is the total closure of affected excavations and/or drifts. The objective of this paper is to illustrate the development and results of a pillar vulnerability index, built through a machine learning classification model, in order to estimate collapserisky areas in production sectors and future projects that are to be put in operation in the coming years, given the 34 years of history of primary ore extraction at El Teniente mine.
