Mine workers operate heavy equipment while experiencing varying psychological and physiological impacts caused by fatigue. These impacts vary in scope and severity across operators and unique mine operations. Previous studies show the impact of fatigue on individuals, raising substantial concerns about the safety of operation. Unfortunately, while data exist to illustrate the risks, the mechanisms and complex pattern of contributors to fatigue are not understood sufficiently, illustrating the need for new methods to model and manage the severity of fatigue’s impact on performance and safety. Modern technology and computational intelligence can provide tools to improve practitioners’ understanding of workforce fatigue. Many mines have invested in fatigue monitoring technology (PERCLOS, EEG caps, etc.) as a part of their health and safety control system. Unfortunately, these systems provide “lagging indicators” of fatigue and, in many instances, only provide fatigue alerts too late in the worker fatigue cycle. Thus, the following question arises: can other operational technology systems provide leading indicators that managers and front-line supervisors can use to help their operators to cope with fatigue levels? This paper explores common data sets available at most modern mines and how these operational data sets can be used to model fatigue. The available data sets include operational, health and safety, equipment health, fatigue monitoring and weather data. A machine learning (ML) algorithm is presented as a tool to process and model complex issues such as fatigue. Thus, ML is used in this study to identify potential leading indicators that can help management to make better decisions. Initial findings confirm existing knowledge tying fatigue to time of day and hours worked. These are the first generation of models and future models will be forthcoming.

The frequent best practice for managing large low-grade run-of-mine (ROM) stockpiles is to average the entire stockpile to only one grade. Modern ore control and mineral processing procedures need better precision. Low-precision models hinder the ability to create a digital mine-to-mill model and optimize the holistic mining process. Prior to processing, poorly characterized stockpiles are often drilled and sampled, despite there being no geological reason for relationships between samples to exist. Stockpile management is also influenced by reserve accounting and lacks a common operational workflow. This paper provides a review of base and precious metal run-of-mine (ROM) pre-crusher stockpiles in the mining industry, and demonstrates how to build a spatial model of a large long-term stockpile using fleet management system (FMS) data and geostatistical code in Python and R Studio. We demonstrate a framework for modelling a stockpile believed to be readily workable for most modern mines through use of established geostatistical modelling techniques applied to the type of data generated in a FMS. In the method presented, each bench of the stockpile is modeled as its own geological domain. Size of dump loads is assumed to contain the same volume of material and grade values that match those of the grade data tracked in the FMS. Despite the limitations of these inputs, existing interpolation techniques can lead to increased understanding of the grade distribution within stockpiles. Using the framework demonstrated in this paper, engineers and stockpile managers will be able to leverage operational data into valuable insight for empowered decision making and smoother operations.

Many factors influence the fatigue state of human beings, and fatigue has a significant adverse effect on the health and safety of the haulage operators in the mine. Among various fatigue monitoring systems in mine operations, currently, the Percentage of Eye Closure (PERCLOS) is common. However, work and other environmental factors influence the fatigue state of haul truck drivers; PERCLOS systems do not consider these factors in their modeling of fatigue. Therefore, modeling work and environmental factors’ impact on individual operations fatigue state could yield interesting insights into managing fatigue. This study provides an approach of using operational data sets to find the leading indicators of the operators’ fatigue. A machine learning algorithm is used to model the fatigue of the individual. eXtreme Gradient Boosting (XGBoost) algorithm is chosen for this model because of its efficiency, accuracy, and feasibility, which integrates multiple tree models and has stronger interpretability. A significant number of negative and positive samples are created from the available data to increase the number of datasets. Then, the results are compared with other existing models. A selected algorithm, along with a big data set was able to create a comprehensive model. The model was able to find the importance of the individual factors along with work and environmental factors among operational data sets.