2023
DOI: 10.3390/geotechnics3040066
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Back-Analysis of Structurally Controlled Failure in an Open-Pit Mine with Machine Learning Tools

Alison McQuillan,
Amichai Mitelman,
Davide Elmo

Abstract: Over the past decades, numerical modelling has become a powerful tool for rock mechanics applications. However, the accurate estimation of rock mass input parameters remains a significant challenge. Machine learning (ML) tools have recently been integrated to enhance and accelerate numerical modelling processes. In this paper, we demonstrate the novel use of ML tools for calibrating a state-of-the-art three-dimensional (3D) finite-element (FE) model of a kinematic structurally controlled failure event in an op… Show more

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Cited by 2 publications
(3 citation statements)
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“…The joint boundary stiffness is a numerical construct that simulates relative slip within an FE mesh. There are no definitive guidelines for accurately assessing the normal and shear stiffness values, which should be calibrated for each analysis [17]. Additionally, a slip failure criterion was applied to the joint elements.…”
Section: Assumptions and Methodologymentioning
confidence: 99%
See 1 more Smart Citation
“…The joint boundary stiffness is a numerical construct that simulates relative slip within an FE mesh. There are no definitive guidelines for accurately assessing the normal and shear stiffness values, which should be calibrated for each analysis [17]. Additionally, a slip failure criterion was applied to the joint elements.…”
Section: Assumptions and Methodologymentioning
confidence: 99%
“…The height H varied from 3-8 m with 1 m increments. [17]. Additionally, a slip failure criterion was applied to the joint elements.…”
Section: Numerical Investigationmentioning
confidence: 99%
“…The application of AI algorithms, such as Artificial Neural Networks (ANN) and Machine Learning (ML), has been increasingly recognized to enhance understanding and prediction in various engineering domains [17][18][19], and contribute to the prediction of service life expectancy and life cycle assessment costs. Despite this, few studies have integrated AI algorithms with the aim of achieving a more accurate projection of the service life and deterioration of building components, along with their service life of building components.…”
Section: Introductionmentioning
confidence: 99%