Application of Bayesian Belief Networks to selection of critical slope movement criteria in open pits

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Large open pit copper/molybdenum mines in British Columbia routinely monitor the performance and response of pit walls to on-going mining activities. The primary monitoring techniques involve the use of reflective prisms, total stations, and wire-line extensometers. More recently, these have been supplemented with ground-based synthetic aperture radar measurements. Unfortunately, decision makers at the mines have difficulty interpreting the significance of the measured deformations. The challenge is to interpret the trends and magnitudes of the measured displacements such that meaningful decisions can be made and appropriate and timely operational responses implemented that minimize production disruptions while ensuring the safety of the workplace. The proposed research will develop and apply a novel Bayesian Belief Network model to identify pit wall movement criteria that should be used by open pits in BC. The movement criteria developed within the Bayesian Belief methodology will be tailored for specific geotechnical conditions and pit wall configurations. The selection of appropriate displacement thresholds will allow the mines to minimize operational delays while ensuring a safe working environment in the pit.