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Quarry sites are complex systems that involve several heavy machines, equipment, people, and management systems working together in an unstructured off-road environment. Gaining accurate insights about these sites requires integrating models at various levels to enable a holistic view systems and processes involved and facilitate effective planning, coordination, and decision-making. In this paper, a multi-level modelling framework is proposed to provide an overall structure for the modelling of quarry sites. The motivation for this framework is drawn from insights gained through a large manufacturing company in the heavy-duty vehicle industry, providing a practical perspective on the modeling approach. The framework integrates models of different operations on site enabling effective simulation and optimization and leading to better understanding of the workflow on site and pointing out any possible bottlenecks. The feasibility of the proposed framework was validated through workshops that included a panel of experts in different areas of the field of off-road machinery production company.

Automating the bucket-filling task in wheel loaders is challenging due to the complex, nonlinear interaction between the bucket and granular material. This work presents a model-based reinforcement learning approach to optimize the bucket-filling strategy for Zeux, Volvo’s autonomous electric wheel loader concept. A Long Short-Term Memory (LSTM) surrogate model is trained on data from Volvo’s high-fidelity simulator to emulate realistic dynamics, enabling efficient policy training using Proximal Policy Optimization (PPO) with imagined rollouts. This reduces computational cost and eliminates the need for direct interaction with the high-fidelity simulator. Compared to Volvo’s current rule-based driver model, the learned policy achieves 89% improvement in productivity and 56% increase in energy efficiency. Our results show that world models can accelerate reinforcement learning for heavy machinery control, enabling the discovery of strategies that outperform controllers based on human expert behavior.

Quarry sites present unique operational challenges where the performance of heavy machinery is critical for maintaining efficiency and safety. In such environments, accurate torque prediction is essential for effective engine management and optimal task execution. This work addresses the torque prediction challenge for a wheel loader operating in quarry conditions by proposing a structured three-phase approach to feature selection that reduces model complexity while preserving predictive accuracy. In the first phase, features are selected based on domain expertise to capture the physical and operational realities of quarry machinery. A comprehensive set of features is then employed to establish a robust performance baseline. In the final phase, a data-driven analysis using SHapley Additive Explanations (SHAP) identifies the top five features that most significantly impact torque prediction. Model efficacy was validated via cross-validation, with R-squared and mean-squared error serving as the key performance indicators. Comparative analysis reveals that while SHAP-ranked features yield statistically optimal results, the expert-selected features are more aligned with the practical requirements of quarry operations. These findings support the design of efficient, interpretable digital twins for real-time decisions in challenging environments.

The Construction and mining Industry comprises complex operations and interactions between various actors at different levels. Simulation has emerged as a valuable tool in this domain to better understand the site's behavior and optimize its operation. However, developing a simulation platform that can handle all the operations on the site is challenging due to the computational cost of the digital representation of reality along with the required accuracy level. This paper aims at extracting and mapping the optimization requirements of construction sites at three main levels: site level, operational level and dynamics level. More precisely, this work seeks to define and map the most important requirements between these levels that ensure simulation credibility and reliability. Based on interviews with experts in the domain, both from academia and industry, several key insights and recommendations emerged: at the site level, the layout and the key performance indicators, such as productivity, time, cost, number of machines and workers, need to be modeled and simulated. At the operational level, the simulation platform must include the main activities, such as loading, excavating, transporting and dumping. Moreover, the dynamics level should involve machine models and their interactions with the site's environment, such as earthmoving, drilling, excavating and blasting.