Abstract
Maintaining a pavement in good condition is one of the key challenges faced by services responsible for road infrastructure. In particular, the poor quality of the surface may pose a serious threat to life and health and lead to serious damage to vehicles. The problem becomes much more serious in difficult environmental and road conditions. Moreover, carrying out an inappropriate policy of maintaining the surface of road infrastructure results in high repair costs and an increase in traffic jams. A similar problem is observed on the access routes and the haulage roads in the mine, where the quality of the roads determines optimal and safe production. One of the components of the mine’s efficiency is the reliability of the wheeled transport fleet. The poor quality of roads leads to high dynamic overloads on the machine and serious damage to its structural nodes. In this article, the authors propose a method for assessing the quality of a road dedicated to underground mining. The algorithm is based on data from the inertial sensors (IMU) installed on mining haul trucks and an on-board monitoring system. The deployed procedure consists of a three-state classification of vibration acceleration readings adapting to the driving speed using machine learning techniques. It is a crusial from the viewpoint of an automatic evaluation of the pavement quality. Using the inertial navigation algorithm enables to plot the road quality on the estimated motion path against the background of mining excavations. In this way, it is possible to obtain a holistic insight into the technical condition of the road infrastructure, which is key for further optimizing production or scheduling road repair works.
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This work is a part of the project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 780883.
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Skoczylas, A., Stefaniak, P., Anufriiev, S., Jachnik, B. (2021). Road Quality Classification Adaptive to Vehicle Speed Based on Driving Data from Heavy Duty Mining Vehicles. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_67
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DOI: https://doi.org/10.1007/978-3-030-68154-8_67
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