From Manual to Automated: The Evolution of Mine Shaft Inspections

The mining industry plays a pivotal role in extracting essential materials that are integral to our everyday lives and the global economy. The archaeological evidence suggests that mining activities may have originated around 43,000 years ago, as indicated by the hematite mines in Swaziland, which are some of the oldest known mines in the world (Dold, 2020). Currently, the mining industry operates an estimated 35,000 mines worldwide (Federal Ministry of Agriculture, Regions and Tourism, 2020), with these operations collectively extracting approximately 16.9 billion tonnes of minerals annually (Idoine et al., 2024). These figures highlight the extensive reach and impact of the global mining sector.

During its evolution, the safety and efficiency of mining operations have always been paramount concerns, given the industry's inherent risks. Despite advancements in safety protocols and technology over the years, mining accidents still occasionally result in fatal injuries (Noraishah, Azizan & Hanida, 2021). According to the International Labour Organization, the mining industry remains one of the most hazardous sectors in terms of occupational health and safety (Carr, 1991).

One of the critical aspects of ensuring safety in underground mining operations is the rigorous inspection of mine shafts. This process is vital for identifying potential hazards that could lead to accidents or fatalities and disruptions or stoppage of operations. Over time, the approach to mine shaft inspection has evolved dramatically. Inspections were traditionally manual and labor-intensive, requiring personnel to physically enter and assess the conditions of the shafts. However, with technological advancements, the industry has shifted towards more innovative, technology-driven solutions.

Modern mine shaft inspections employ various technologies to conduct thorough and efficient assessments with tools such as drones equipped with cameras and sensors, robotic systems, and advanced monitoring software. These technologies may help detect issues that aren’t necessarily visible or accessible to human inspectors, like structural weaknesses, gas leaks, and water ingress.

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The Traditional Approach: Visual Inspections

Traditionally, mine shaft inspections have predominantly been a manual process, reliant on the skills, expertise, and knowledge of inspectors (Teleky, 1948). These professionals, armed with basic tools like flashlights and measuring tapes, would venture into mine shafts to assess their condition. This traditional method, while rooted in the hands-on experience of inspectors, came with inherent risks and inefficiencies. It has often exposed workers to hazardous underground conditions such as toxic gases, unstable structures, and limited escape routes in case of emergency.

Despite the inspectors’ dedication and skills, the limitations of visual inspections are evident. The process isn’t only time-consuming, but also prone to human error, as it heavily relies on individual judgment and experience. The subjective nature of these inspections may lead to inconsistent and sometimes inaccurate data, which complexifies the tasks of maintenance planning and risk management.

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The Shift Towards Technology: Early Innovations

In response to the growing awareness of the limitations and risks associated with traditional manual mine shaft inspections, the mining industry, particularly throughout the last century, embarked on a quest for technological advancements. This pursuit aimed to enhance the safety and efficiency of these critical evaluations (Cashman, 2022), marking a significant shift in the industry's approach to ensuring operational integrity and worker safety.

During this transformative period, various mechanical and electronic devices were introduced to assist in the inspection process. These innovations provided a more systematic and data-driven approach to measuring and assessing the structural integrity of mine shafts, as well as evaluating their air quality. 

Early technological tools in mine shaft inspections included plumb lines and levels, which were used to assess the verticality and alignment of shaft walls. While these tools represented an improvement over purely visual inspections, their effectiveness was still heavily dependent on the mine’s environmental conditions and periodical inspections from the personnel, which limited their reliability (Browne, 1949, Vala & Seligova, 2013).

Additionally, more sophisticated devices like infrared thermal imaging cameras were developed to detect loose rocks or areas prone to collapse within mine shafts. These tools however required inspectors to be in close proximity to potentially dangerous areas, still exposing them to significant risks (Radl, Mitra, & Clausen, 2022). The level of accuracy and precision of such technologies were also heavily dependant on the physical conditions of the mine (Iverson, 2014).

Geophones, as an early form of seismic measurement equipment, represented a more advanced approach. By measuring the velocity of shock waves through mine walls, they provided insights into the structural integrity of the shafts. However, these devices also required expert operation and interpretation, maintaining a level of manual involvement and potential for human error.

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The Game Changer: Automated 3D Scanning

A pivotal advancement in mine shaft inspection came with the introduction of automated 3D laser scanning technologies (Van der Merwe & Andersen, 2013), which significantly transformed and revolutionized the way mine shafts are evaluated. These innovative technologies enable comprehensive, non-intrusive scanning of mines, and produce detailed 3D models that offer an unprecedented level of detail for analysis. This leap in technology has drastically reduced risks to miners by providing reliable inspection data (Van der Merwe & Andersen, 2013).

Point Laz is proud to be taking part in this revolution with the development of advanced scanning systems. Our 3D laser scanner, the Lazaruss, exemplifies cutting-edge developments in the mining industry.

The Lazaruss scanner not only automates the scanning process, but also employs sophisticated algorithms to analyze the data and identify potential structural issues. This capability is a game-changer for the industry, streamlining maintenance by enabling predictive maintenance strategies. By identifying potential problems before they escalate, mines can reduce unplanned downtime and enhance operational efficiency.

The automation of mine shaft inspections marks a significant milestone in enhancing mining safety. By removing humans from direct exposure to hazardous conditions and providing detailed, accurate data, technologies like the Lazaruss scanner support proactive maintenance strategies, thereby enhancing the overall productivity of mining operations.

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The Future of Mine Shaft Inspection

The future of mine shaft inspections is on the edge of a significant transformation, driven by the rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies. These advanced technologies are set to redefine the landscape of mine shaft inspections, transcending conventional automation to achieve unprecedented levels of accuracy, predictability, and safety. As AI and ML algorithms become more sophisticated given the ever-growing data available for training acquired from on-premise technologies like the Lazaruss 3D scanner, they promise to empower the mining industry with unrivaled predictive capabilities (Bołoz & Witold, 2020). This would allow for the identification and mitigation of potential hazards before they materialize, thus shifting the focus from reactive to proactive management of mine safety and structural integrity.

Moreover, the application of machine learning algorithms heralds the advent of predictive maintenance strategies. These strategies leverage historical and ongoing inspection data to forecast the longevity and maintenance needs of mine shaft infrastructure and machinery. Predictive models could pinpoint the timing and location of required maintenance, optimizing resource use and reducing operational interruptions. This shift toward predictive maintenance should not only improve safety and efficiency but also systematically reduce the likelihood of unforeseen failures (Kruczek et al., 2019).

As these technologies advance and become more integrated into the practice of mine shaft inspections, they promise to set a new benchmark for industry practices.

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Embracing Innovation with Point Laz

At Point Laz, we believe that the Lazaruss 3D scanner is a testament to innovation in mining. It revolutionizes the way mine shaft inspections are done by merging safety, efficiency, and advanced technology, while embodying the industry's commitment to progress and worker protection.

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References: 

Bołoz, Ł. and Witold B. 2020. "Automation and robotization of underground mining in Poland" Applied Sciences 10, no. 20: 7221. https://doi.org/10.3390/app10207221

Browne, L. D. (1949). Movements of Freely Swinging Plumb-Lines in Deep Vertical Shafts. Journal of the Chemical, Metallurgical and Mining Society of South Africa. https://journals.co.za/doi/pdf/10.10520/AJA0038223X_4563

Carr, T. S. (1991). Underground Mine Disasters: History, Operations and Prevention. Professional Safety, 36(3), 28. https://www.proquest.com/scholarly-journals/underground-mine-disasters-history-operations/docview/200370431/se-2

Cashman (2022). How Technology is Impacting the Mining Industry. https://www.cashmanequipment.com/about/the-dirt-blog/how-technology-is-impacting-the-mining-industry

Dold, B. (2020). Sourcing of critical elements and industrial minerals from mine waste - The final evolutionary step back to sustainability of humankind? Journal of Geochemical Exploration, 219. https://doi.org/10.1016/j.gexplo.2020.106638

Earth systems. A brief history of mining. https://www.earthsystems.com/history-mining/

Federal Ministry of Agriculture, Regions and Tourism (2020). World mining data 2020 (p. 265) https://world-mining-data.info/wmd/downloads/PDF/WMD2020.pdf#:~:text=URL%3A%20https%3A%2F%2Fworld 

Idoine, N.E.; Raycraft, E.R.; Hobbs, S.F.; Everett, P.; Evans, E.J.; Mills, A.J.; Currie, D.; Horn, S.; Shaw, R.A.. 2024 World mineral production 2018-2022. Nottingham, UK, British Geological Survey, 99pp. (World mineral production). 

https://nora.nerc.ac.uk/id/eprint/537241/1/World%20Mineral%20Production%202018%20to%202022.pdf 

Iverson, S. (2014) Assessment and Detection of Loose Rock Hazards in Underground Metal Mines Using Thermal Imaging. https://onemine.org/documents/assessment-and-detection-of-loose-rock-hazards-in-underground-metal-mines-using-thermal-imaging 

Kruczek, P. et al. (2019). Predictive maintenance of mining machines using an advanced data analysis system based on cloud technology. In: Widzyk-Capehart, E., Hekmat, A., Singhal, R. (eds) Proceedings of the 27th International Symposium on Mining Planning and Equipment Selection - MPES 2018. Springer, Cham. https://doi.org/10.1007/978-3-319-99220-4_38

Noraishah, I. S., Azizan, R., & Hanida, A. A. (2021). Research trends in the study of mining accidents: A systematic literature review. Safety Science, 143. https://doi.org/10.1016/j.ssci.2021.105438

Radl, A., Mitra, R. and Clausen, E. (2022). Loose rock detection methods for automating the scaling process. Mining Technology, 131(4), 249-255. https://doi.org/10.1080/25726668.2022.2078091

Teleky, L. (1948). History of hygiene in factories and mines. New York Chichester, West Sussex: Columbia University Press. https://doi.org/10.7312/tele91424 

Vala, D. and Seligova, D. (2013). Photogrammetry in Mining Shaft Inspection Using MCR Controlled LED and Laser Light Source. IFAC Proceedings Volumes, 45(28), 316-319. https://doi.org/10.3182/20130925-3-CZ-3023.00002

Van der Merwe, J. W. and Andersen, D. C. (2013). Applications and benefits of 3D laser scanning for the mining industry. Journal of the Southern African institute of Mining and Metallurgy, 113(3), 00-00.

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