Volume 74 Issue 280
Machine learning prediction of the load evolution in three-point bending tests of marble
Kaklis, K.; Saubi, O.; Jamisola, R.; Agioutantis, Z.
Machine learning in the form of artificial neural networks was applied to investigate whether specimen load evolution can be predicted as a function of acoustic emission (AE) signals in the case of three-point bending (TPB) marble specimens instrumented with piezoelectric sensors. The ultimate objective of this study is to develop a model that can quantify rock behavior under loading that can lead to rock-failure prediction in underground structures subjected to bending, such as roof failure in development or production openings.
Mining, Metallurgy & Exploration (2022) 39:2037–2045, https://doi.org/10.1007/s42461-022-00674-1
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