Results 41 to 50 of about 15,649 (256)

Gaborlet‐guided sparse filtering: A novel intelligent method for lithology identification by vibration signals while drilling

Deep Underground Science and Engineering, EarlyView.
The flowchart illustrates rock specimen testing, vibration signal acquisition, and feature extraction with Gaborlet and sparse filtering for classification. Abstract Traditional lithology identification methods mainly rely on core sampling and well‐logging data.
Jian Hao, Danqi Zhang, Zhenqi Song, Jinrui Wang, Qi Wang, Heqing Liu   +5 more
wiley   +1 more source

Study on the composite fracture characteristics of filling and reinforcing cracked rock mass after high temperature damage

Deep Underground Science and Engineering, EarlyView.
Cement infill shares compressive and shear stresses during loading, while interfacial friction hinders crack propagation and reduces stress concentration, improving the rock's load‐bearing capacity. Failure modes vary with crack inclination. Unfilled specimens show four modes.
Shihao Yuan, Qiang Sun, Jianjun Hu, Liwei Zhang, Jishi Geng   +4 more
wiley   +1 more source

IoT-based 3D pose estimation and motion optimization for athletes: Application of C3D and OpenPose

Alexandria Engineering Journal
This study proposes the IoT-Enhanced Pose Optimization Network (IE-PONet) for high-precision 3D pose estimation and motion optimization of track and field athletes.
Fei Ren, Chao Ren, Tianyi Lyu
doaj   +1 more source

Modeling and experimental validation of bolt–grout bond–slip behavior for deep underground excavation support

Deep Underground Science and Engineering, EarlyView.
A novel shear‐lag model, validated by pull‐out tests, incorporates a constant bond stress stage to predict the complete load–displacement response of grouted rock bolts, providing a new tool for optimizing anchorage length and material toughness in deep underground engineering. Abstract The stability of rock masses in deep underground excavations, such
Wenhui Bian, Xiaohui He, Kexue Wang, Yufei Wu, Guoxin Wang, Shaochen Zhang, Jun Yang   +6 more
wiley   +1 more source

Real-time monitoring of lower limb movement resistance based on deep learning

Alexandria Engineering Journal
Real-time lower limb movement resistance monitoring is critical for various applications in clinical and sports settings, such as rehabilitation and athletic training.
Burenbatu, Yuanmeng Liu, Tianyi Lyu
doaj   +1 more source

Research progress and current status of dynamic wave propagation characteristics in rock mass: A review

Deep Underground Science and Engineering, EarlyView.
This review elucidates the velocity–dispersion–attenuation coupling mechanisms of wave propagation in rock masses, compares six representative models, and reveals how pressure, temperature, mineral composition, and anisotropy jointly control dynamic responses in complex geological media.
Jiajun Shu, Tao Li, Ruiqi Yuan, Yue Li, Bingni Wu, Bo Liu, Zhengding Deng, Galindo Rubén, Fausto Molina‐Gómez   +8 more
wiley   +1 more source

Thermal‐induced fracture evolution and acoustic signature of Beishan granite: Insights into tensile strength of barrier system for high‐level radioactive waste disposal

Deep Underground Science and Engineering, EarlyView.
The tensile strength of granite, a proposed host for nuclear waste, degrades severely above 400°C. We link this to specific microcracking patterns captured by acoustic emission, establishing a scientific basis for assessing repository safety under thermal load.
Wei Zeng, Shijie Li, Xiaozhao Li, Zhen Huang, Shangtong Yang, Wentao Xu, Biao Wang, Yun Wu   +7 more
wiley   +1 more source

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

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

Experimental Study on the Mechanical Properties of Deep Granite under Gradient-Confining Pressure

Applied Sciences
In deep layers, the complex geological environment is characterized by high temperature and high stress which causes marked differences in the mechanical properties of granite compared to those of granite in shallow areas.
Shaolong Qin, Xingdong Zhao, Wenlong Yu, Jingyi Song, Tong Wu   +4 more
doaj   +1 more source