Doctor of Philosophy in Geological Engineering (PhD)
Mud rush Risk Management in Block Cave Mining
Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.
Rapid landslides pose a significant hazard worldwide, and there is currently no routine way of predicting the impact area and velocities of these catastrophic events. Increased development in marginal areas is changing the landslide risk in many parts of the world. There is an urgent need for practical methods to predict the motion of these tragic events to cope with this changing risk. Practical methods currently in use rely on simplified landslide statistics that have a high degree of uncertainty, and are often unable to predict landslide velocities. The focus of this thesis is on developing practical methods to reliably predict the motion of rapid landslides so that public safety in landslide prone areas can be improved.This thesis makes extensive use of runout modelling in order to analyse the motion of rock avalanches, debris avalanches and flowslides. The work presented here can be broadly divided into two categories; the development of new tools and techniques to model flow-like landslide motion, and the compilation and analysis of a database of case histories. The new tools include: 1) A new rheology appropriate for the simulation of liquefied materials; 2) A new dynamic model to simulate the initially-coherent motion of some rock and debris avalanches; 3) Two new calibration methodologies. These techniques were then applied to a database of rock avalanches, debris avalanches and flowslide case histories in order to infer movement mechanisms and give guidance for forward prediction. The main findings include: 1) The character of the path materials is a plausible explanation for the mechanism governing rock avalanche motion. Based on this, a probabilistic framework to predict rock avalanche motion was suggested; 2) A back-analysis of a fatal debris avalanche that occurred in British Columbia in 2012 revealed that this flow was likely moving in an undrained condition, which had significant implications for the analysis of its motion; 3) It was found that flowslides can occur in fine grained colluvium, and this material should be recognized as potentially liquefiable.
Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
Forecasting the spatial impact of debris flows is challenging due to complex runout behaviour, such as variable mobility and channel avulsions. Practitioners often base the probability of runout exceedance on a fan, or define avulsion scenarios, on judgement. To support decision making, spatial impact trends were studied at thirty active debris flow fans in southwestern British Columbia (SWBC), Canada. 176 debris flow impact areas covering an average observation period of 74 years were mapped using orthorectified historical airphotos, satellite imagery, topographic basemaps, lidar, and field observations. A graphical plotting method was developed that converts geospatial mapping to spatial impact heatmaps normalized by the fan boundary, allowing for comparison of runout trends across fans in the dataset. Probability of spatial impact was analyzed in two components: runout down-fan (i.e., how far debris flows tend to travel past the apex toward the fan toe) and runout cross-fan (i.e., how far debris flows tend to deviate from the previous flow path). For fans in SWBC, there is a characteristic decay in spatial impact probability from the fan apex and the previous flow path, represented by a normal and log-normal distribution for normalized runout in the down-fan and cross-fan components, respectively. Differences in spatial impact trends can be explained, in part, by event volume, Melton ratio, fan truncation, and fan activity, however not by fan morphometrics, such as the slope or the point at which channelization is lost. A tool was created that transposes the empirical runout distributions onto a fan to assist in risk-based decision making. Future work may involve fitting functions to the spatial impact data for a more robust and adaptable forecasting tool. Supplementary materials available at: http://hdl.handle.net/2429/77299