Characterisation for spatial distribution of mining-induced stress through deep learning algorithm on SHM data-Reference-Cited by-同舟云学术

Characterisation for spatial distribution of mining-induced stress through deep learning algorithm on SHM data

Published:2023-01-02 Issue:1 Volume:17 Page:217-226
ISSN:1749-9518
Container-title:Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards
language:en
Short-container-title:Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards

Author:

Tan Xuyan¹²^ORCID,Chen Weizhong¹²,Qin Changkun¹²,Zhao Wusheng¹²,Ye Wei¹²

Affiliation:

1. State Key Laboratory of Geomechanics and Geotechnical Engineering, Chinese Academy of Sciences, Institute of Rock and Soil Mechanics, Wuhan, People’s Republic of China

2. Institute of Rock and Soil Mechanics, University of Chinese Academy of Sciences, Beijing, People’s Republic of China

Funder

National Natural Science Foundation of China

Chinese Academy of Sciences

Publisher

Informa UK Limited

Subject

Geology,Geotechnical Engineering and Engineering Geology,Safety, Risk, Reliability and Quality,Building and Construction,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/17499518.2023.2172188

Reference36 articles.

1. Autonomous CNN (AutoCNN): A data-driven approach to network architecture determination

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