Engineers should verify design assumptions for pit slopes regularly through geotechnical monitoring data to ensure safety and economical performance during open-pit mining. In the first place, one should know some important definitions. The design of pit slopes provides information about slope angles to obtain the largest possible ore volume without causing wall collapse (Kolapo et al., 2022). It greatly depends on geological, hydrogeological, and rock mass properties assumptions made by engineers. Geotechnical monitoring refers to the measurement of deformation of rock mass during mining.
Validation is necessary due to the presence of geological uncertainty that naturally exists in nature. At the very first phases of feasibility, design for the slopes is done based on borehole testing and lab results that form only a minor percentage of what would actually be found in reality (Hodgkinson & Elmouttie, 2020). From such little information engineers try to make a comprehensive 3D model of the mine site with huge assumptions regarding structural discontinuities and hydrological systems of the region.
Validation is controlled by the observational technique and follows a sequence of steps including prediction, observation, and adjustment. Before carrying out any excavation work, numerical models will be used to create models of the slopes and predict the way they behave. The model prediction sets the theoretical limit that serves as a benchmark when comparing actual observations. Numerical models help geotechnical engineers test the potential failures of the geological system (Shapka-Fels & Elmo, 2022).
Precision data collection plays a critical role in this validation process. Modern opencast mines employ advanced monitoring systems that can measure physical deformations and pressures with high precision. The surface deformations are monitored through the use of slope stability radar, laser scanner, and total station systems that collect spatial data continuously. At the same time, the subsurface deformation is measured through inclinometers and piezometers. The data collection through such an effective system provides engineers with the ability to create an accurate geotechnical environment with smart decisions.
Back-analysis through numbers is the link that bridges empirical monitoring with model validation. In the event monitoring systems notice any abnormality such as unforeseen spikes in pore pressures or rapid rock movements, back-analysis takes place. The numerical models are fed with field data so as to determine the true geomechanics parameters. It’s through continuous feeding of ground models with real data from the field that assumptions on material strengths are corrected hence improving the engineering designs (Wang & Tian, 2023).
Finally, the comparison of pit slope design assumptions with results from geotechnical monitoring activities is an iterative and important process that will make sure that the design becomes a living reality. The process turns a stationary design into something real which can be analyzed, and by constantly making changes to models that predict what is going to happen next, safety at mining sites will be ensured.
References
Hodgkinson, J. H., & Elmouttie, M. (2020). Cousins, Siblings and Twins: A Review of the Geological Model’s Place in the Digital Mine. Resources, 9(3), 24. https://doi.org/10.3390/resources9030024
Kolapo, P., Oniyide, G. O., Said, K. O., Lawal, A. I., Onifade, M., & Munemo, P. (2022). An Overview of Slope Failure in Mining Operations. Mining, 2(4), 350–384. https://doi.org/10.3390/mining2020019
Shapka-Fels, T., & Elmo, D. (2022). Numerical Modelling Challenges in Rock Engineering with Special Consideration of Open Pit to Underground Mine Interaction. Geosciences, 12(5), 199. https://doi.org/10.3390/geosciences12050199
Wang, Y., & Tian, H. (2023). Digital geotechnics: from data-driven site characterisation towards digital transformation and intelligence in geotechnical engineering. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 18(1), 8–32. https://doi.org/10.1080/17499518.2023.2278136
