April 10, 2024International Journal of Rock Mechanics and Mining SciencesOpen Access

Prediction of rockfall hazard in open pit mines using a regression based machine learning model

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Authors

ISI.P. SenanayakeUniversity of Newcastle Australia PHPhilipp HartmannFriedrich-Alexander-Universität Erlangen-Nürnberg AGAnna GiacominiUniversity of Newcastle Australia

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Implication

Simulation study demonstrates regression models predict rockfall impact energy and run-out distances in open pit mines, indicating potential for automated hazard assessment.

Key Points

Multi-linear regression accurately predicts impact kinetic energy, whereas multi-non-linear regression best models first impact position and final rockfall run-out.
Probabilistic rockfall simulations on 15 highwalls calibrated models using 4 walls and validated across 11 walls, extracting slope local roughness and angles.
3D photogrammetric models enable automated extraction of geometric profiles, highlighting a viable method for rapid rockfall hazard assessment across mines.

Cite This Study

Senanayake et al. (2024) studied this question.

synapsesocial.com/papers/68e6fa8ab6db643587674afe https://doi.org/10.1016/j.ijrmms.2024.105727

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Implication

Simulation study demonstrates regression models predict rockfall impact energy and run-out distances in open pit mines, indicating potential for automated hazard assessment.

Prediction of rockfall hazard in open pit mines using a regression based machine learning model

Discussion

Member takes

Implication

Key Points

Cite This Study

Prediction of rockfall hazard in open pit mines using a regression based machine learning model

Discussion

Member takes

Implication

Key Points

Cite This Study

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