Experimental data - driven algorithm to predict muckpile characteristics in jointed overburden bench using unmanned aerial vehicle and AI tools

In the current investigation, an intriguing method called Firefly-XGBoost was put up to predict and integrate blast muck-pile results notably drop, throw, and lateral spread, which are instrumental in regulating and remedying the loading problems by loader as well as deceiving factors in type of loa...

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Vydáno v:International journal of mining, reclamation and environment Ročník 38; číslo 8; s. 642 - 676
Hlavní autoři: Chandrahas, N. Sri, Fissha, Yewuhalashet, Choudhary, Bhanwar Singh, Olamide Taiwo, Blessing, Venkataramayya, M. S., Adachi, Tsuyoshi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 13.09.2024
Taylor & Francis Ltd
Témata:
AI-Muckpile
Algorithms
artificial intelligence application
blast fragmentation
firefly-XG boost algorithm
Firing pattern
Heuristic methods
Loaders
muck-pile parameters
Overburden
Particle swarm optimization
unmanned aerial vehicle
Unmanned aerial vehicles
ISSN:1748-0930, 1748-0949
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Shrnutí:In the current investigation, an intriguing method called Firefly-XGBoost was put up to predict and integrate blast muck-pile results notably drop, throw, and lateral spread, which are instrumental in regulating and remedying the loading problems by loader as well as deceiving factors in type of loader selection. As a result, the particle swarm optimisation (PSO) algorithm and the firefly algorithm were relied on to shore up the efficacy of the XG Boost conventional model. A total of 164 blast experiments were executed in two different mines and the data collected from these trials data were used to train the model. This data includes specific features such as joints spanning height (JSH) as well as other blast characteristics such as the number of joint sets, decking length, total quantity of explosives, stemming length, decking length and firing pattern. The Firefly-XG Boost algorithm yielded better outcomes in terms of RMSE and R 2 values when compared to the XG Boost, and PSO-XG Boost algorithms. The developed model was found to be suitable in predicting muck-pile parameters for practical application.
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ISSN:1748-0930
1748-0949
DOI:10.1080/17480930.2024.2340876