[HTML][HTML] Prediction of geological characteristics from shield operational parameters by integrating grid search and K-fold cross validation into stacking classification …

T Yan, SL Shen, A Zhou, X Chen - Journal of Rock Mechanics and …, 2022 - Elsevier
This study presents a framework for predicting geological characteristics based on
integrating a stacking classification algorithm (SCA) with a grid search (GS) and K-fold cross …

Machine learning for rock mechanics problems; an insight

H Yu, AD Taleghani, F Al Balushi… - Frontiers in Mechanical …, 2022 - frontiersin.org
Due to inherent heterogeneity of geomaterials, rock mechanics involved with extensive lab
experiments and empirical correlations that often lack enough accuracy needed for many …

A Review of Orebody Knowledge Enhancement Using Machine Learning on Open-Pit Mine Measure-While-Drilling Data

DM Goldstein, C Aldrich, L O'Connor - Machine Learning and Knowledge …, 2024 - mdpi.com
Measure while drilling (MWD) refers to the acquisition of real-time data associated with the
drilling process, including information related to the geological characteristics encountered …

Penetration rate prediction for diamond bit drilling by adaptive neuro-fuzzy inference system and multiple regressions

H Basarir, L Tutluoglu, C Karpuz - Engineering Geology, 2014 - Elsevier
In many mining, civil, and petroleum engineering applications diamond bit drilling is widely
used due to high penetration rate, core recovery and its ability to drill with less deviation …

[HTML][HTML] Prediction of the uniaxial compressive strength of rocks from simple index tests using a random forest predictive model

M Wang, W Wan, Y Zhao - Comptes …, 2020 - comptes-rendus.academie-sciences …
Uniaxial compressive strength (UCS) is an important mechanical parameter for stability
assessments in rock mass engineering. In practice, obtaining the UCS simply, accurately …

Application of artificial neural networks and multivariate statistics to predict UCS and E using physical properties of Asmari limestones

M Torabi-Kaveh, F Naseri, S Saneie… - Arabian journal of …, 2015 - Springer
Geomechanical properties of rocks such as uniaxial compressive strength (UCS) and
modulus of elasticity (E) have been essentially evaluated for rock engineering projects as …

Monitor-While-Drilling-based estimation of rock mass rating with computational intelligence: The case of tunnel excavation front

M Galende-Hernández, M Menéndez, MJ Fuente… - Automation in …, 2018 - Elsevier
The construction of tunnels has serious geomechanical uncertainties involving matters of
both safety and budget. Nowadays, modern machinery gathers very useful information about …

[HTML][HTML] Improved prediction of shear wave velocity for clastic sedimentary rocks using hybrid model with core data

MI Miah - Journal of Rock Mechanics and Geotechnical …, 2021 - Elsevier
Accurate measurement of acoustic velocities of sedimentary rocks is essential for prediction
of rock elastic constants and well failure analysis during drilling operations. Direct …

Prediction of strength parameters of sedimentary rocks using artificial neural networks and regression analysis

Y Abdi, AT Garavand, RZ Sahamieh - Arabian Journal of Geosciences, 2018 - Springer
Accurate laboratory measurement of geo-engineering properties of intact rock including
uniaxial compressive strength (UCS) and modulus of elasticity (E) involves high costs and a …

Predictive modeling of the uniaxial compressive strength of rocks using an artificial neural network approach

X Wei, NM Shahani, X Zheng - Mathematics, 2023 - mdpi.com
Sedimentary rocks provide information on previous environments on the surface of the
Earth. As a result, they are the principal narrators of the former climate, life, and important …