# Geomechanical Basis for Assessing Open-Pit Slope Stability in High-Altitude Gold Mining
## 3 Discussion  
The research conducted into the geomechanical stability of open-pit slopes at the Bozymchak deposit presents significant insights into both practical monitoring techniques and theoretical evaluation frameworks. The results are consistent with earlier works on slope stability assessment, such as the methodologies described by [PERSON] and [PERSON] [7] and further expanded by recent numerical studies [8; 20].  
One of the critical outcomes of this research is the comprehensive application of in situ tests on rock mass prisms, which yielded reliable cohesion and internal friction angle values for marbleized limestones and granodiorites. This approach aligns well with the field methods described by [PERSON] et al. [5] and supports the ongoing refinement of shear testing as a diagnostic tool in rock engineering.  
The introduction and validation of the \"reverse calculation method\" for evaluating the strength properties of rock contacts marks a significant advancement in slope stability diagnostics. Using this approach, the authors derived the average cohesion and friction parameters for fractured surfaces lined with iron hydroxides--a feature also observed in similar studies of pit wall behavior under tectonic influence [21]. The application of limit equilibrium equations to real-world collapse cases aligns with the practices advocated by [PERSON], [PERSON] [22] and allows for parameter estimation even in inaccessible or previously collapsed rock masses.  
It is noteworthy that seismic events were incorporated into the stability modeling scenarios. The influence of a hypothetical magnitude 9 seismic event reduced the factor of safety of the northern slopes from 1.31-2.90 to as low as 1.14, though still within permissible limits [8; 16]. This consideration reflects international best practices in seismic slope evaluation, as discussed in [PERSON], [PERSON] [20] and [PERSON] et al. [19], and highlights the importance of conservative design principles for long-term slope integrity.  
Trends in the numerical modeling of slope behavior have been emphasized in multiple references [8; 10; 22; 23], where finite element methods and geomechanical block models have provided reliable predictive tools for pit design. The work at Bozymchak successfully applies similar modeling schemes validated against actual failure cases, as recommended by the Serdaliev E.T.; [9] and Serikbayev EKTU [15]. The successful matching of simulated outcomes with observed displacements adds a layer of credibility to the selected models.  
Another practical outcome of the study is the development and implementation of a remote geodetic monitoring system using Leica GeoMoS instrumentation. This automated system enables the real-time tracking of marker point displacement vectors in three dimensions. Its integration is fully aligned with modern digital monitoring practices, as summarized in recent systematic reviews [22; 23].  
Regarding operational recommendations, the study confirms the efficacy of pre-split blast methods in preserving slope integrity. Ensuring a specific charge concentration of 0.3-0.5 kg/m\({}^{3}\) was shown to reduce fracturing and back-break along slope faces, which corresponds with findings from Suichang Gold Mine case studies [24; 25; 26]. These blasting practices are essential for maintaining sharp slope profiles and avoiding unintended overbreak, especially in weakly cemented or fractured zones.  
In conclusion, the Bozymchak slope stability project demonstrates an integrative methodology combining empirical testing, inverse analytical techniques, seismic impact modeling, and real-time monitoring [27]. The outcomes are congruent with both classical and modern geomechanical theories and provide a replicable model for slope management in similar geological settings. Future research may benefit from incorporating machine learning algorithms for pattern recognition in displacement data and expanding the database of local collapse events to improve predictive capabilities.