Results 251 to 260 of about 123,387 (338)

Advancements in predicting soil liquefaction susceptibility: a comprehensive analysis of ensemble and deep learning approaches. [PDF]

Sci Rep
Ranjan Kumar D, Wipulanusat W.
europepmc   +1 more source

A lightweight binocular vision‐supported framework for 3D structural dynamic response monitoring

Computer-Aided Civil and Infrastructure Engineering, EarlyView.
Abstract Current three‐dimensional (3D) displacement measurement algorithms exhibit practical limitations, such as computational inefficiency, redundant point cloud data storage, reliance on preset targets, and restrictions to unidirectional measurements. This research aims to address computation efficiency and accuracy issues in binocular camera‐based
Yujie Ruan, Tao Huang, Cheng Yuan, Gang Zong, Qingzhao Kong   +4 more
wiley   +1 more source

AVO reflectivity and pre-stack seismic impedance inversion for gas sand channel detection at South Abu El Naga Field, Onshore Nile Delta, Egypt. [PDF]

Sci Rep
Al-Ashqar NA, El-Werr AK, Helaly AS, Kamel A.   +3 more
europepmc   +1 more source

Three‐dimensional morphological analysis of Chang'e‐5 lunar soil using deep learning‐automated segmentation on computed tomography scans

Computer-Aided Civil and Infrastructure Engineering, EarlyView.
Abstract Grain morphology is a fundamental characteristic of lunar soil that influences its mechanical properties, sintering behavior, and in situ resource utilization. However, traditional two‐dimensional imaging methods are time‐consuming and lack full three‐dimensional (3D) structural information. This study presents an automated deep learning‐based
Siqi Zhou, Yu Jiang, Xinyang Tao, Feng Li, Chi Zhang, Wei Yang, Yangming Gao   +6 more
wiley   +1 more source

Probabilistic evaluation of combination rules for seismic response prediction of horizontally curved RC bridges under varying earthquake incidence angles. [PDF]

Sci Rep
Tehrani P, Heydarpour K.
europepmc   +1 more source

Mmin - Implications of its choice for Canadian seismic hazard and seismic risk

, 2010
S Halchuk, J Adams
openalex   +1 more source

Deep learning for computer vision in pulse‐like ground motion identification

Computer-Aided Civil and Infrastructure Engineering, EarlyView.
Abstract Near‐fault pulse‐like ground motions can cause severe damage to long‐period engineering structures. A rapid and accurate identification method is essential for seismic design. Deep learning offers a solution by framing pulse‐like motion identification as an image classification task.
Lu Han, Zhengru Tao
wiley   +1 more source

3D structure and dynamics of Campi Flegrei enhance multi-hazard assessment. [PDF]

Nat Commun
De Landro G, Vanorio T, Muzellec T, Russo G, Lomax A, Virieux J, Zollo A.   +6 more
europepmc   +1 more source

SEISMIC RISK INFORMATION FOR WOODEN HOUSE OWNERS PROVIDED ON WEB SITE

, 2011
Yasuhiro Mori, Keiko SHIMADA, N. Ogura, Hideki IDOTA, Kota IBUKI, Shozo TERADA   +5 more
openalex   +2 more sources

Large language model for post‐earthquake structural damage assessment of buildings

Computer-Aided Civil and Infrastructure Engineering, EarlyView.
Abstract A rapid and accurate assessment of structural damage to buildings in the aftermath of earthquakes is critical to emergency responses and engineering retrofit decisions. However, current in situ building damage assessment is primarily conducted through visual inspections by engineering professionals and deep learning techniques using single ...
Yongqing Jiang, Jianze Wang, Xinyi Shen, Kaoshan Dai   +3 more
wiley   +1 more source