Results 201 to 210 of about 64,749 (299)

Probabilistic prediction of rate‐dependent rock strength using natural gradient boosting and Gaussian process regression

Deep Underground Science and Engineering, EarlyView.
Probabilistic natural gradient boosting and Gaussian process regression models accurately predict rate‐dependent rock strength across lithologies. Static strength and strain rate dominate, while geometric factors have minimal influence, enabling interpretable and uncertainty‐aware predictions for dynamic geomechanical applications. Abstract The dynamic
Hadi Fathipour‐Azar
wiley   +1 more source

Innovative Use of Stone Waste for Sustainable Fertility Management in Tropical Soils. [PDF]

ACS Omega
Borges RC, Freitas TSM, Fernandes JO, Zonta E, Nascimento IG, Cincotto FH, Pereira MG, Pinto LADSR, Bernardino CAR.   +8 more
europepmc   +1 more source

Rock mechanical properties under reconstructed deep in situ thermo–hydro–mechanical conditions: Implications for CO₂‐related injection scenarios

Deep Underground Science and Engineering, EarlyView.
The graphical abstract illustrates a reconstructed in situ thermo‐hydro‐mechanical (THM) framework in which porosity serves as the central variable linking stress, pore pressure, and temperature to evolving mechanical properties of rocks. Under burial conditions, in situ stress, pore pressure, and temperature jointly govern volumetric strain and ...
Mingyuan Lu, Jun Wang, Zixiong Qi, Ruixuan Chen, Hongtao Wang, Hongwei Zhou   +5 more
wiley   +1 more source

Heterogeneous Wettability Alters Methane Migration and Leakage in Shallow Aquifers. [PDF]

Environ Sci Technol
Khandoozi S, Gautam S, Dietsch C, Sahimi M, Cole D, Soltanian R.   +5 more
europepmc   +1 more source

Explainable hybrid stacking ensemble method for hard rock pillar stability prediction and engineering applications

Deep Underground Science and Engineering, EarlyView.
This research proposes an interpretable hybrid stacking ensemble framework, optimized by the Sparrow Search Algorithm, to enhance hard rock pillar stability prediction. By integrating six machine learning models—k‐nearest neighbors, support vector machines, random forests, Gradient Boosting Decision Tree, eXtreme Gradient Boosting, and Light Gradient ...
Ning Wang, Guoyan Zhao, Meng Wang, Taraka Pattela   +3 more
wiley   +1 more source

A Photomicrographic Dataset of Rocks for the Accurate Classification of Minerals. [PDF]

Sci Data
Amer BG, Mousa HM, Dawoud M, Youssef A.
europepmc   +1 more source

TAMNet: Temporal and adaptive‐frequency network with MixStyle for cross‐region oil and fluid production forecasting

Deep Underground Science and Engineering, EarlyView.
This paper presents temporal and adaptive‐frequency network with MixStyle (TAMNet), a deep time‐series modeling framework for accurate and robust multi‐well oil productivity forecasting. TAMNet integrates transformer and long short‐term memory architectures to capture both short‐ and long‐term temporal dependencies, enhanced by a temporal gate unit ...
Chunxi Yang, Leifeng Meng, Qinghu Fan, Ziang Guo, Ting Bao, Pingdong Zhong, Fulin Luo   +6 more
wiley   +1 more source

Evaluating the impact of CO₂ on the geomechanical and geochemical properties of different rock types. [PDF]

RSC Adv
Dontoh WH, Alhajeri MM, Mews K, Tsau JS, Goldstein R, Barati R.   +5 more
europepmc   +1 more source

Investigation on the failure evolution characteristics of surrounding rock in deep cross‐fault roadway: Based on physical model test and numerical simulation

Deep Underground Science and Engineering, EarlyView.
This study reveals the failure evolution characteristics of deep cross‐fault roadway surrounding rock under excavation support and periodic weighting. Periodic weighting readily induces fault activation, with the spatial distribution of failed rock masses being controlled by the fault strike and dip.
Tiezhu Li, Suping Peng, Jing Zheng, Zhanhua Yang, Qijun Tian   +4 more
wiley   +1 more source