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Enhancing output in open-pit coal mining: The influence of front loading geometry on machinery functionality

Chairul Salam M et al.

Concurrent Engineering Research and Applications2025https://doi.org/10.1177/1063293x251361192article
AJG 1
Weight
0.37

What the paper says

This study examines the productivity of excavation and haulage equipment in overburden removal operations at the Borneo Indobara site, with a primary focus on optimizing front-loading geometry, improving material flow, and enhancing equipment performance through advanced modeling and real-time monitoring. Particular emphasis was placed on the optimization of front-loading geometry, material flow dynamics, and equipment configurations to enhance overall operational efficiency and reduce inefficiencies across excavation cycles. A novel framework integrating machine learning, metaheuristic algorithms, and IoT-enabled real-time monitoring was implemented to predict productivity, improve bucket fill factors, and reduce cycle times. Results demonstrated that optimal front dimensions and innovative loading patterns significantly increased productivity. Gradient Boosting and Particle Swarm Optimization were employed for precise modeling and resource allocation, while dynamic simulations validated the proposed solutions. The findings highlight the potential for advanced data-driven methods to mitigate inefficiencies and enhance performance in open-pit mining operations.

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https://doi.org/https://doi.org/10.1177/1063293x251361192

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@article{chairul2025,
  title        = {{Enhancing output in open-pit coal mining: The influence of front loading geometry on machinery functionality}},
  author       = {Chairul Salam M et al.},
  journal      = {Concurrent Engineering Research and Applications},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/1063293x251361192},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.