Fusion of finite element and machine learning methods to predict rock shear strength parameters

D Zhu, B Yu, D Wang, Y Zhang - Journal of Geophysics and …, 2024 - academic.oup.com
The trial-and-error method for calibrating rock mechanics parameters has the disadvantages
of complexity, being time-consuming, and difficulty in ensuring accuracy. Harnessing the …

Closed-form equation for estimating unconfined compressive strength of granite from three non-destructive tests using soft computing models

AD Skentou, A Bardhan, A Mamou, ME Lemonis… - Rock Mechanics and …, 2023 - Springer
The use of three artificial neural network (ANN)-based models for the prediction of
unconfined compressive strength (UCS) of granite using three non-destructive test …

Orebody cavability prediction challenges in block caving mining—a review

K Suzuki Morales, FT Suorineni… - Bulletin of Engineering …, 2024 - Springer
A traditional block caving mine considers the development of an undercut level that allows
blocky or veined rock masses to fracture, fail, and unravel when the undercut area is large …

[PDF][PDF] Rock Strength Estimation Using Several Tree-Based ML Techniques.

Z Liu, DJ Armaghani, P Fakharian, D Li… - … in Engineering & …, 2022 - cdn.techscience.cn
The uniaxial compressive strength (UCS) of rock is an essential property of rock material in
different relevant applications, such as rock slope, tunnel construction, and foundation. It …

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 …

Mechanical properties of cemented tailings and waste-rock backfill (CTWB) materials: Laboratory tests and deep learning modeling

S Yin, Z Yan, X Chen, R Yan, D Chen, J Chen - Construction and Building …, 2023 - Elsevier
Cemented tailings and waste-rock backfill (CTWB) is an effective way to solve the problem of
mine solid waste. Waste-rock content is an important factor affecting UCS, but fully …

Prediction of uniaxial compressive strength using fully Bayesian Gaussian process regression (fB-GPR) with model class selection

T Zhao, C Song, S Lu, L Xu - Rock Mechanics and Rock Engineering, 2022 - Springer
In rock, mining, and/or tunneling engineering, determination of uniaxial compressive
strength (UCS) of rocks is an important and crucial task, which is often estimated from readily …

Data-driven modelling and evaluation of a battery-pack system's mechanical safety against bottom cone impact

D Xu, Y Pan, X Zhang, W Dai, B Liu, Q Shuai - Energy, 2024 - Elsevier
The exploration of the mechanical responses of the battery pack system (BPS) when
subjected to dynamic impact loads is crucial for the safety of power batteries during …

Experimental study and soft computing modeling of the unconfined compressive strength of limestone rocks considering dry and saturation conditions

S Alzabeebee, DA Mohammed… - Rock Mechanics and Rock …, 2022 - Springer
Unconfined compressive strength (UCS) of intact limestone rocks is very significant in
geotechnical engineering in order to design structures that are currently being built on/in …

Application of KNN-based isometric mapping and fuzzy c-means algorithm to predict short-term rockburst risk in deep underground projects

M Kamran, B Ullah, M Ahmad, MMS Sabri - Frontiers in Public Health, 2022 - frontiersin.org
The rockburst phenomenon is the major source of the high number of casualties and
fatalities during the construction of deep underground projects. Rockburst poses a severe …