2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 21 , Issue 2 , PP: 56-69, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Raed Majeed 1 * , Hiyam Hatem 2
Doi: https://doi.org/10.54216/FPA.210204
The Fourth Industrial Revolution represents a shift to a more connected, digital world across all industries, including mining. The application of smart sensors will reduce site risks and fuel consumption, reduce equipment breakdowns, improve preventative maintenance, and improve equipment efficiency, including dump truck engines. Dump truck fuel efficiency is influenced by a number of real-world factors, including driver behavior, road and weather conditions, and vehicle specifications. Additionally, potential engine failures and other aspects can impact vehicle outcomes. By using dynamic on-road data to predict fuel consumption per trip, the industry can effectively minimize the expense associated with driving evaluations. Furthermore, analysis of data provides valuable insights into identifying the underlying causes of fuel consumption by analyzing input parameters. This paper proposes and evaluates novel models for predicting dump truck fuel consumption and engine failures in open-pit mining. These models combine the power of features derived from data collected locally by dump truck sensors and their analysis. The fuel consumption prediction architecture for open-top mining trucks using an improved Long Short-Term Memory (LSTM) model and a double-layer thick Deep Neural Network (DNN) forms the basis of the model design, which consists of two separate components. Multi-delay Recurrent Neural Network (RNN) models have been found to be efficient and accurate. The RNN architecture is applied to capture the cyclic components and complex rules in engine consumption data. This research relied on essential factors (route, vehicle speed, engine revolutions, and engine load). The proposed model outperforms existing models, achieving (MAE=0.0210), (RMSE=0.0294), (MSE=0.0009), and accuracy (R²=0.9842), demonstrating that it can produce highly accurate predictions.
Recurrent Neural Networks (RNNs) , Fuel Consumption , Engine Failures , Deep Learning (DL) , Mining Dump Trucks
[1] Klaus Schwab. “The fourth industrial revolution, Crown Business”. In: New York 192 (2017).
[2] Li Da Xu, Eric L Xu, and Ling Li. “Industry 4.0: state of the art and future trends”. In: International journal of production research 56.8 (2018), pp. 2941–2962.
[3] Raymond Leung, Andrew J Hill, and Arman Melkumyan. “Automation and Artificial Intelligence Technology in Surface Mining: A Brief Introduction to Open-Pit Operations in the Pilbara”. In: IEEE Robotics & Automation Magazine (2023).
[4] Luis Rojas, ´Alvaro Pe˜na, and Jos´e Garcia. “AI-driven predictive maintenance in mining: a systematic literature review on fault detection, digital twins, and intelligent asset management”. In: Applied Sciences 15.6 (2025), p. 3337.
[5] Oscar Serradilla et al. “Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects”. In: Applied Intelligence 52.10 (2022), pp. 10934–10964.
[6] Bartosz Orzeł and Radosław Wolniak. “Digitization in the design and construction industry—remote work in the context of sustainability: a study from Poland”. In: Sustainability 14.3 (2022), p. 1332.
[7] Neeraj Dhanraj Bokde et al. “Total dissolved salt prediction using neurocomputing models: case study of gypsum soil within Iraq region”. In: IEEE Access 9 (2021), pp. 53617–53635.
[8] H Aguirre-Jofr´e et al. “Low-cost internet of things (IoT) for monitoring and optimising mining small- scale trucks and surface mining shovels”. In: Automation in Construction 131 (2021), p. 103918.
[9] Omar Y Al-Jarrah et al. “Efficient machine learning for big data: A review”. In: Big Data Research 2.3(2015), pp. 87–93.
[10] Alex Graves. “Generating sequences with recurrent neural networks”. In: arXiv preprint arXiv:1308.0850
(2013).
[11] Quanzeng You et al. “Image captioning with semantic attention”. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, pp. 4651–4659.
[12] Elnaz Siami-Irdemoosa and Saeid R Dindarloo. “Prediction of fuel consumption of mining dump trucks: A neural networks approach”. In: Applied Energy 151 (2015), pp. 77–84.
[13] Thomas Bousonville, Martin Dirichs, and Thilo Kr¨uger. “Estimating truck fuel consumption with machine learning using telematics, topology and weather data”. In: 2019 International Conference on Industrial Engineering and Systems Management (IESM). IEEE. 2019, pp. 1–6.
[14] Lalit Kumar Sahoo, Santanu Bandyopadhyay, and Rangan Banerjee. “Benchmarking energy consumption for dump trucks in mines”. In: Applied energy 113 (2014), pp. 1382–1396.
[15] Dmytrychenko Mykola and Svietazarov Oleksandr. “Theoretical Justification of Factors of Influence on Fuel Consumption of Dump Trucks in the Process of Unloading Bulk Cargo”. In: World Science 2(2022), p. 553186.
[16] Thomas Bousonville et al. “Data driven analysis and forecasting of medium and heavy truck fuel consumption”. In: Enterprise Information Systems 16.6 (2022), p. 1856417.
[17] Yi Zhou et al. “Prediction model of fuel consumption of heavy truck based on improved BP neural network”. In: 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). IEEE. 2022, pp. 1–6.
[18] Chenxi Chen et al. “Research on vehicle fuel consumption prediction model based on Cauchy mutation multiverse algorithm”. In: 2022 9th International Forum on Electrical Engineering and Automation (IFEEA). IEEE. 2022, pp. 1115–1119.
[19] Gonc¸alo Pereira et al. “Fuel consumption prediction for construction trucks: A noninvasive approach using dedicated sensors and machine learning”. In: Infrastructures 6.11 (2021), p. 157.
[20] Robert Grega et al. “Failure analysis of driveshaft of truck body caused by vibrations”. In: Engineering Failure Analysis 79 (2017), pp. 208–215.
[21] Xiao-lei Xu and Zhi-wei Yu. “Failure analysis of a truck diesel engine crankshaft”. In: Engineering Failure Analysis 92 (2018), pp. 84–94.
[22] Artem Vesnin, Volodymyr Sistuk, and Anton Bogachevskiy. “The analysis of mining conditions in- fluence to operating time of dump trucks traction drive components”. In: Metallurgical and Mining Industry 3 (2015), pp. 268–271.
[23] Jason Brownlee. Data preparation for machine learning: data cleaning, feature selection, and data transforms in Python. Machine Learning Mastery, 2020.
[24] Aur´elien G´eron. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. ” O’Reilly Media, Inc.”, 2022.
[25] Kedar Potdar, Taher S Pardawala, and Chinmay D Pai. “A comparative study of categorical variable encoding techniques for neural network classifiers”. In: International journal of computer applications 175.4 (2017), pp. 7–9.
[26] Davide Chicco and Giuseppe Jurman. “The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation”. In: BMC genomics 21 (2020), pp. 1–13.
