Simultaneous slope design optimisation and stability assessment using a genetic algorithm and a fully automatic image-based analysis

Mine slope design is a complex task that requires consideration of geotechnical analysis, structural stability, economics and the environment. Economic factors usually drive mine slope design, particularly in the case of open-pit designs, where the process of steepening slope walls by several degrees can have profound financial implications. Due to the risks associated with catastrophic slope collapse, slope stability analysis is an integral component of open-pit engineering projects. However, initial design concepts and geotechnical assessments are often considered separately. In this study, a technique is developed that combines the scaled boundary finite element method (SBFEM) with genetic algorithms (GAs) to simultaneously perform slope stability analysis and optimise the slope profile. The iterative design approach optimises characteristics of the slope profile such as the slope height, width, angle and number of benches while ensuring the factor of safety (FoS) remains above a threshold value. A salient feature of the technique is the ability to automatically address the modifications to the geometry of the slope by updating the digital images used in the analysis to assess the stability of each instance in the optimisation process and determine the optimum slope geometry. The results highlight the application of the developed technique to determine appropriate slope excavation designs as well as slope backfilling scenarios. The method is exemplified in several cases where complex stratigraphies and spatially variable materials are considered. As such, the GA-driven slope design process conveys an optimised, automated tool, combining mine slope design and slope stability analysis.