Fuel-Efficient Road Classification Methodology for Sustainable Open Pit Mining
The roughness of haul roads significantly impacts fuel consumption in open-pit coal mine trucks, yet there is currently a lack of quantitative road classification methods in this regard. This study proposes a fuel-efficient road classification methodology for open-pit coal mines. Using UAV-captured...
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/11/6309 |
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| author | Boyu Luan Wei Zhou Zhogchen Ao Zhihui Han Yufeng Xiao |
| author_facet | Boyu Luan Wei Zhou Zhogchen Ao Zhihui Han Yufeng Xiao |
| author_sort | Boyu Luan |
| collection | DOAJ |
| description | The roughness of haul roads significantly impacts fuel consumption in open-pit coal mine trucks, yet there is currently a lack of quantitative road classification methods in this regard. This study proposes a fuel-efficient road classification methodology for open-pit coal mines. Using UAV-captured point cloud data of mine roads as the basis for roughness analysis and the International Roughness Index (IRI) as the evaluation metric, the research establishes linear relationships between IRI and fuel consumption for both loaded and unloaded trucks. The K-means clustering algorithm is employed to classify road quality into “good”, “moderate”, and “poor” categories, with the Haerwusu Open-pit Coal Mine serving as a case study. Results demonstrate that 150 m represents an appropriate IRI segmentation interval for Haerwusu, with IRI thresholds of 12 (15) and 20 (21) serving as critical segmentation points for loaded (unloaded) trucks. From analyzing two end-slope roads in the case study mine we found that upgrading “poor” roads to “moderate” quality could reduce fuel costs by 3% for loaded trucks and 2% for unloaded trucks. This study provides a quantitative road classification method for open-pit coal mines, offering a theoretical foundation for reducing transportation costs and promoting sustainable mining development. |
| format | Article |
| id | doaj-art-4b5f495fe3264b279e94a4cf63beb5c6 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-4b5f495fe3264b279e94a4cf63beb5c62025-08-20T02:23:00ZengMDPI AGApplied Sciences2076-34172025-06-011511630910.3390/app15116309Fuel-Efficient Road Classification Methodology for Sustainable Open Pit MiningBoyu Luan0Wei Zhou1Zhogchen Ao2Zhihui Han3Yufeng Xiao4School of Mines, China University of Mining and Technology, Xuzhou 221116, ChinaState Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mines, China University of Mining and Technology, Xuzhou 221116, ChinaThe roughness of haul roads significantly impacts fuel consumption in open-pit coal mine trucks, yet there is currently a lack of quantitative road classification methods in this regard. This study proposes a fuel-efficient road classification methodology for open-pit coal mines. Using UAV-captured point cloud data of mine roads as the basis for roughness analysis and the International Roughness Index (IRI) as the evaluation metric, the research establishes linear relationships between IRI and fuel consumption for both loaded and unloaded trucks. The K-means clustering algorithm is employed to classify road quality into “good”, “moderate”, and “poor” categories, with the Haerwusu Open-pit Coal Mine serving as a case study. Results demonstrate that 150 m represents an appropriate IRI segmentation interval for Haerwusu, with IRI thresholds of 12 (15) and 20 (21) serving as critical segmentation points for loaded (unloaded) trucks. From analyzing two end-slope roads in the case study mine we found that upgrading “poor” roads to “moderate” quality could reduce fuel costs by 3% for loaded trucks and 2% for unloaded trucks. This study provides a quantitative road classification method for open-pit coal mines, offering a theoretical foundation for reducing transportation costs and promoting sustainable mining development.https://www.mdpi.com/2076-3417/15/11/6309mine roadroad classificationhaulage road roughnessmine transportation |
| spellingShingle | Boyu Luan Wei Zhou Zhogchen Ao Zhihui Han Yufeng Xiao Fuel-Efficient Road Classification Methodology for Sustainable Open Pit Mining Applied Sciences mine road road classification haulage road roughness mine transportation |
| title | Fuel-Efficient Road Classification Methodology for Sustainable Open Pit Mining |
| title_full | Fuel-Efficient Road Classification Methodology for Sustainable Open Pit Mining |
| title_fullStr | Fuel-Efficient Road Classification Methodology for Sustainable Open Pit Mining |
| title_full_unstemmed | Fuel-Efficient Road Classification Methodology for Sustainable Open Pit Mining |
| title_short | Fuel-Efficient Road Classification Methodology for Sustainable Open Pit Mining |
| title_sort | fuel efficient road classification methodology for sustainable open pit mining |
| topic | mine road road classification haulage road roughness mine transportation |
| url | https://www.mdpi.com/2076-3417/15/11/6309 |
| work_keys_str_mv | AT boyuluan fuelefficientroadclassificationmethodologyforsustainableopenpitmining AT weizhou fuelefficientroadclassificationmethodologyforsustainableopenpitmining AT zhogchenao fuelefficientroadclassificationmethodologyforsustainableopenpitmining AT zhihuihan fuelefficientroadclassificationmethodologyforsustainableopenpitmining AT yufengxiao fuelefficientroadclassificationmethodologyforsustainableopenpitmining |