Haul road roughness measurement using georeferenced truck vibration

• Autorzy: Douglas Alexander

Identyfikatory

DOI

10.2478/ntpe-2019-0044

• Języki publikacji: EN

Abstrakty

EN

Vehicle vibrations caused by poor haul road conditions create multiple negative effects for mines, including slower cycle times, increased maintenance, and operator injury. Vibration levels in vehicles result in part from road roughness. Mine roads are mainly constructed from in-pit materials that are more likely to deteriorate overtime and require frequent maintenance to maintain a smooth surface. The decision for when and where road maintenance is conducted is primarily based on visual inspections. This method can provide subjective, inaccurate, and delayed response to adverse conditions. The recent increase in vehicle telemetry data allows instant access to several types of data; mainly being used for haul fleet dispatching, collision avoidance, and geologic surveying, telemetry data has yet to see widespread use in road maintenance dispatching. This paper examines current road roughness characterization techniques and current telemetry data streams. An initial case study was conducted using vibration and Global Navigation Satellite System (GNSS) telemetry data to determine road roughness. Data from three haul trucks under normal operating conditions were collected over the course of a week. The results of this case study demonstrate localized vibration levels can be used to objectively identify rough roads. This can be further developed to dispatch road maintenance crews leading to overall reduced mining costs and increased operator health. The researches propose continuing to full scale test using data from an entire fleet and longer timeframe.

Słowa kluczowe

EN

haul road; vibration; operator health; road maintenance; Big Data

PL

warunki drogowe; wibracja; nierówności drogi; roboty drogowe; Big Data

• Wydawca: STE GROUP ; De Gruyter
• Czasopismo: New Trends in Production Engineering
• Rocznik: 2019
• Tom: Vol. 2, Iss. 1
• Strony: 416--423
• Opis fizyczny: Bibliogr. 9 poz., rys., tab.

Twórcy

autor

Douglas Alexander

• Missouri University of Science and Technology, USA

Bibliografia

• 1. Bovenzi, M., Hulshof, C. T., & Hulshof, M. B. C. T. J. (1999). An updated review of epidemiologic studies on the relationship between exposure to whole-body vibration and low. Int. Arch. Occup. Environ. Health, 72(0340-0131 SB-IM), 351–365.
• 2. Federal Highway Administration, United States Department of Transportation. (2015). Gravel Roads Construction and Maintenance Guide.
• 3. Hugo, D., Heyns, S. P., Thompson, R. J., & Visser, A. T. (2007). Condition-Triggered Maintenance for Mine Haul Roads with Reconstructed-Vehicle Response to Haul Road Defects. Transportation Research Record: Journal of the Transportation Research Board, 1989-2(1), 254-260.
• 4. Hugo, D., Heyns, P. S., Thompson, R. J., & Visser, A. T. (2008). Haul road defect identification using measured truck response. Journal of Terr mechanics, 45(3), 79-88.
• 5. Kotchon, A.C., Lipsett, M.G. & Nobes, D.S. (2016) Journal of Failure Analysis and Prevention 16: 438.
• 6. Ngwangwa, H. M., and Heyns, P. S. (2014). Application of an ANN-based methodology for road surface condition identification on mining vehicles and roads. Journal of Terr mechanics, 53(1), 59-74.
• 7. Ngwangwa, H. M., Heyns, P. S., Breytenbach, H. G. A., & Els, P. S. (2014). Reconstruction of road defects and road roughness classification using Artificial Neural Networks simulation and vehicle dynamic responses: Application to experimental data. Journal of Terr mechanics, 53(1), 1-18.
• 8. Thompson, R. J. (2018). Using big data to predict haul road performance: Engineering, geology, mineralogy, metallurgy, chemistry, etc. Engineering and Mining Journal, 219(3), 34-37.
• 9. Wolfgang, R., and Burgess-Limerick, R. (2014). Whole-body vibration exposure of haul truck drivers at a surface coal mine. Applied Ergonomics, 45(6), 1700-1704.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).