With open pit mines now reaching depths of more than a kilometre, slope stability, failure mitigation, and effective monitoring of pit walls are becoming increasingly complex and crucial for safe and productive operations.
The consequences of inadequate slope design and stability monitoring can be severe. In the two years leading up to 2020, five slope failures were reported across open-pit mines in Western Australia, resulting in substantial damage to assets and infrastructure. During the same period, slope failures at mines in Queensland and the Northern Territory tragically led to two fatalities.
Given these risks, slopes must be designed and monitored effectively to ensure mine safety and operational continuity.
Slope failures rarely occur without warning; they typically develop progressively due to cumulative displacement over time.
This provides the opportunity to detect slope failure indicators early, enabling mine operators to intervene and mitigate any potential harm.
Slopes can be monitored in two broad categories based on data acquisition methods: subsurface monitoring, which measures movements deep within the weathered material of open-pit slopes, and surface monitoring, which detects visible signs of instability and quantifies surface-level deformations and displacements.
A range of internal factors that reduce the inherent strength of a slope can cause it to fail, including geological discontinuities, weak rock masses, and weathering.
External triggers include heavy rainfall infiltration and groundwater variation, which increase pore water pressure and reduce shear strength; seismic activity, which can induce vibrations that destabilise rock masses; and mining-induced disturbances such as blasting and excavation, which can undercut or overload the slope and disrupt its equilibrium.
These factors can cause one of four primary failure mechanisms for open-pit slopes – plane, wedge, circular, or toppling failure.
A review published earlier this year in the journal Mining examined existing and emerging methods for open-pit slope stability monitoring. The study aimed to identify the underlying differences in performance across key attributes, including monitoring accuracy, spatial and temporal coverage, operational complexity, and economic viability.
This included both remote sensing and in situ methods, as well as advanced technologies such as artificial intelligence, the Internet of Things, and wireless sensor networks.
Slope stability monitoring involves detecting and tracking the rate of slope displacement, as an increasing rate often indicates the onset of incipient slope failure.
One of the benefits of using emerging technologies is their ability to enhance failure detection and provide predictive analytics. While visual inspection remains a widely reliable method for early identification, it can expose workers to hazardous environments and is inherently susceptible to human error.
Furthermore, visual inspection is unsuitable for high-risk or rapid failure zones.
The juxtaposition created by the review highlighted the reality that no universally optimal or ‘gold standard’ slope stability monitoring system exists, due to compromises from inherent technological limitations and site-specific constraints.
The researchers said: “Remote sensing methods offer large-scale, non-intrusive monitoring, but are often limited by environmental factors and data acquisition infrequency, whereas in situ methods provide high precision but suffer from limited spatial coverage and scalability.”
The findings revealed that the bulk of contemporary research focused on remote sensing technologies and advanced modalities, while exploring the use of drones equipped with digital photogrammetry tools.
The researchers described these methods as broadly accurate, precise, and effective at detecting and monitoring slope failure indicators, such as geological displacement and visual signals like scarping, ravelling, and cracking.
They added: “In situ methods, conversely, are far less common in state-of-the-art literature.
“Despite this, the review results indicate that reliable geotechnical instruments, such as time domain reflectometry, extensometers, piezometers, and inclinometers, are unanimously praised for their effectiveness in tracking and quantifying subsurface slope displacement trends, despite their rudimentary operating principles and methodologies.
“Emerging technologies, such as artificial intelligence, machine learning, the Internet of Things, and wireless sensor networks, act as a mediator between the old and the new, providing enhanced predictive monitoring capabilities and continuous real-time stability monitoring.”