November 2022
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.

Full-text paper:
Mining, Metallurgy & Exploration (2022) 39:2037–2045,


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