GRNN-FA hybrid model for predicting the useful life of dump truck tyres

• Autorzy: Kunkyin-Saadaari Festus , Kesseh Alfred , Agadzie Victor Kwaku

Identyfikatory

DOI

10.46873/2300-3960.1451

• Języki publikacji: EN

Abstrakty

EN

The useful life of dump truck tyres is crucial for optimising economic efficiency and safety in mining operations. This study employs advanced hybrid models, including a generalised regression neural network (GRNN) combined with an artificial bee colony (ABC), ant colony optimisation (ACO), and firefly algorithm (FA), to predict tyre life at Perseus Mining Ghana Limited to enhance prediction accuracy. Sensitivity analysis using simple linear regression (SLR) identified tread depth as the most influential factor, with a correlation coefficient of 0.9638. Among the models, GRNN-FA demonstrated superior performance, achieving the highest correlation coefficient (r = 0.9586) and lowest mean squared error (MSE = 0.1019), root mean squared error (RMSE = 0.3191) and, mean absolute error (MAE = 0.2761). The study highlights that improving operational factors such as floor and road conditions, payload weight, and cycle time can significantly extend the tyre life. Implementing the GRNN-FA model can optimise tyre management, reduce downtimes, and enhance operational efficiency, thereby setting a new standard for tyre life prediction in the mining industry.

Słowa kluczowe

EN

dump truck tyre life; generalized regression neural network; ant colony optimisation; artificial bee colony; firefly algorithm; simple linear regression

PL

żywotność opon do wywrotek; uogólniona sieć neuronowa regresji; optymalizacja kolonii mrówek; sztuczna kolonia pszczół; algorytm świetlika; prosta regresja liniowa

• Wydawca: Elsevier ; Główny Instytut Górnictwa
• Czasopismo: Journal of Sustainable Mining
• Rocznik: 2025
• Tom: Vol. 24, iss. 2
• Strony: 227--249
• Opis fizyczny: Bibliogr. 89 poz.

Twórcy

autor

Kunkyin-Saadaari Festus

• University of Mines and Technology, Department of Mining Engineering, Ghana

• https://orcid.org/0000-0002-8202-2021

autor

Kesseh Alfred

• University of Mines and Technology, Department of Mining Engineering, Ghana

• https://orcid.org/0009-0004-4427-7666

autor

Agadzie Victor Kwaku

• University of Mines and Technology, Department of Mining Engineering, Ghana

• https://orcid.org/0000-0001-9616-3725

Bibliografia

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