Mining Technology Vol. 134, No. 2, out now

Advanced research from the underground – featuring a reassessment of Janssen’s equation for cave stress estimation, AI-driven dilution prediction, digital twin adoption in the mining Metaverse and more.

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Reassessing Janssen's equation for cave stress estimation in block cave mining
Yalin Li, Davide Elmo
pp. 95–104
DOI: 10.1177/25726668251337293

Dilution prediction in underground open stope mining using gene expression programming and backpropagation artificial neural network algorithms
Prosper Chimunhu, Roohollah Shirani Faradonbeh, Erkan Topal, Mohammad Waqar Ali Asad, Ajak Duany Ajak
pp. 105–120
DOI: 10.1177/25726668251348707

Evaluation of the technology acceptance model of digital twins supported by artificial intelligence in the mining Metaverse: A partial least squares structural equation modelling analysis
Alejandro Marcelo Acosta-Quelopana, Phillip Stothard, Mario Gustavo Berrios-Espezúa, José Julián Rodríguez-Delgado
pp. 121–142
DOI: 10.1177/25726668251348711

Dynamic multi-period mixed-integer non-linear programming model for equipment selection in the mining industry
Sena Senses, Mustafa Kumral
pp. 143–158
DOI: 10.1177/25726668251348712

Characterisation of blastability variation within drill holes using a recurrent neural network with reduced supervision, generative transformer assisted training
Rachel Xu, Chris Elders, Ebrahim Fathi-Salmi, Ewan J. Sellers, Faris Azhari
pp. 159–186
DOI: 10.1177/25726668251348708