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Deep Neural Network Models for Improving Truck Productivity Prediction in Open-pit Mines

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Abstract

The accurate prediction of truck productivity plays a pivotal role in improving the efficiency and profitability of open-pit mining operations. However, predicting truck productivity is challenging owing to the complex nature of the working conditions of the mine site. This paper proposes a deep neural network model to overcome the challenge of predicting truck productivity in open-pit mines. The prediction model was built using eight variables and was optimized by considering different train-test split ratios, numbers of hidden layers and neurons, and activation functions. The proposed model's performance was evaluated using various metrics and was compared with other commonly used machine learning algorithms. According to the results, the proposed model outperformed traditional machine learning algorithms by achieving higher accuracy and lower error rates, with the best-performing model having four hidden layers with 70 neurons per layer and a scaled exponential linear unit activation function, resulting in a coefficient of determination value of 0.89. This demonstrates the potential of deep neural network models for predicting truck productivity in open-pit mine sites. Moreover, a single variable sensitivity analysis was conducted to investigate the impact of input variables on truck productivity. The results show that haul distance is the most influential variable for the prediction of truck productivity.

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Data Availability

The data that support the findings of this study are available from the corresponding author, [WVL], upon reasonable request.

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Acknowledgments

The University of Alberta supported this project through a Collaborative Research Project [RES0043251] and a Pilot Seed Grant [RES0049944]. Omer Faruk Ugurlu is now affiliated with the Mining Engineering Department at Istanbul University - Cerrahpasa, Turkey.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, T6G 2E3, Canada

    Omer Faruk Ugurlu, Chengkai Fan & Wei Victor Liu

  2. Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, T6G 2G1, Canada

    Bei Jiang

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  1. Omer Faruk Ugurlu
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Contributions

Omer Faruk Ugurlu: Generation of concepts, Research design, Implementation of code, Writing - original draft, Writing - review & editing, Data representation.

Chengkai Fan: Initiation of Research Gap and Innovation, Generation of concepts, Research design, Writing - review & editing.

Bei Jiang: Supervision, Writing - review & editing.

Wei Victor Liu: Initiation of Research Gap and Innovation, Resources, Supervision, Writing - review & editing, Funding acquisition.

Corresponding authors

Correspondence to Bei Jiang or Wei Victor Liu.

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Omer Faruk Ugurlu and Chengkai Fan contributed equally and shared the order of first authors.

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Ugurlu, O.F., Fan, C., Jiang, B. et al. Deep Neural Network Models for Improving Truck Productivity Prediction in Open-pit Mines. Mining, Metallurgy & Exploration 41, 619–636 (2024). https://doi.org/10.1007/s42461-024-00924-4

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