This work presents a new method of mine ventilation network optimisation as standard non-linear programming problem and discusses the use of a novel first-order Lagrangian (FOL) algorithm for equality constraints as a solution tool for these problems. Slack variables have been defined to transform such inequality constraints into their corresponding equality forms. The problem is then converted to non-linear problem with equality constrains. The methodology adopted in this paper is capable of dealing with the non-linear convex model with significant savings on computational efforts due to its use of only first derivatives. A MATLAB programme has been developed based on the FOL method to solve a generalised mine ventilation network optimisation problem. To study the validity and the viability of the FOL programme, the programme has been applied to already published network problems and both results are identical.

Trucks experience excitations during haulage due to road roughness, generating dynamic loads. Current mine haul road design techniques assume static tire loads, ignoring dynamic forces. This paper presents mathematical models for estimating tire dynamic forces on haul roads. Models were solved in Simulink® and RStudio® to generate random road profile (class D) according to ISO 8608, and compute dynamic forces for 59/80R63 tire. Results show that road roughness significantly affects impact forces on roads, with tire dynamic forces (1638.67 kN) ~ 1.6 times static forces (~1025 kN) at rated tire payloads. The method presented gives realistic estimates of tire impact forces, which serves as useful input for haul road design.

The shovel-truck system is used in surface mines due to its flexibility, low-cost operations, maintainability and multi-face operating capability. Technological advances have resulted in large shovels and trucks for bulk, economic operations causing high-impact shovel loading operations (HISLO). The resulting HISLO impact forces cause significant vibrations, which are transmitted through the truck frame into an operator's cabin. Long-term exposure to these vibrations could cause lower back injuries, spine injuries and other musculoskeletal diseases. Previous research has focused on truck dynamic simulation using multi-body dynamics and finite element method within the haulage segment of the production chain. This article focuses on truck vibrations in the dumping process. Mathematical models are developed to capture the vibration response of an integrated operator–machine–material system. Virtual prototype models are developed to simulate the response of the integrated system suspension to vibrations in MSC.ADAMS. The results affirm the ineffectiveness of current suspension mechanisms to eliminate shock wave propagation into the operator's cabin during dumping, with adverse impact on operator's safety and health.

Integrating autonomous haulage systems into underground mining has revolutionized safety and operational efficiency. However, deploying 3D detection systems for autonomous truck navigation in such an environment faces persistent challenges due to dust, occlusion, complex terrains, and low visibility. This affects their reliability and real-time processing. While existing reviews have discussed object detection techniques and sensor-based systems, providing valuable insights into their applications, only a few have addressed the unique underground challenges that affect 3D detection models. This review synthesizes the current advancements in 3D object detection models for underground autonomous truck navigation. It assesses deep learning algorithms, fusion techniques, multi-modal sensor suites, and limited datasets in an underground detection system. This study uses systematic database searches with selection criteria for relevance to underground perception. The findings of this work show that the mid-level fusion method for combining different sensor suites enhances robust detection. Though YOLO (You Only Look Once)-based detection models provide superior real-time performance, challenges persist in small object detection, computational trade-offs, and data scarcity. This paper concludes by identifying research gaps and proposing future directions for a more scalable and resilient underground perception system. The main novelty is its review of underground 3D detection systems in autonomous trucks.

Large capacity shovels are deployed in surface mining operations for achieving economic bulk production targets. These shovels use crawler tracks for effective terrain engagement in these environments. Shovel reliability, maintainability, availability and efficiency depend on the service life of the crawler tracks. In rugged and challenging terrains, crawler wear, tear, cracks and failure are extensive resulting in prolonged downtimes with severe economic implications. In particular, crawler shoe wear, tear, cracks and fatigue failures can be expensive in terms of maintenance costs and production losses. No fundamental research has been undertaken to understand the crawler-formation interactions in challenging and rugged terrains in surface mining operations. This study forms the foundations for providing long-term solutions to crawler failure problems. The kinematic equations governing the crawler-formation interactions have been formulated to characterise the crawler motions during shovel production. These equations capture the motions governing the link pin joint, oil sand terrain joint and driving constraints based on the multi-body rigid theory. Crawler propel is achieved by using prescribed velocities along a translational degree of freedom (DOF) and a translational and rotational DOF. The crawler kinematic solutions show that the 3-D crawler–terrain model results in 132 DOFs and requires dynamic modelling to obtain the unknown degrees of freedom. A 3-D virtual prototype model is built to capture the crawler-formation interaction in MSC ADAMS based on the rigid body crawler kinematics. The virtual prototype simulator is supplied with mass properties of crawler shoe, mass, stiffness and damping characteristics of oil sand and external loads due to machine weight and contact forces to obtain the time variation of position, velocity and acceleration for the crawler–terrain engagement for given driving constraints. The results from the driving constraints yield a non-linear longitudinal motion of the crawler track assembly. The crawler track lateral and vertical displacements during translation-only motion fluctuates with maximum magnitudes of 0.7 and 3.6 cm. Similarly the fluctuating longitudinal, lateral and vertical velocities and accelerations have maximum magnitudes of 0.22, 0.046 and 0.56 m/s and 7.41, 1.73, and 34.9 m/s², respectively. This research provides a strong foundation for further study on developing flexible crawler track model for predicting crawler shoes dynamic stress distributions, cracks development and propagation and fatigue analysis during shovel operations.