Prediction of tunnel mechanical behaviour using multi-task deep learning under the external condition-Reference-Cited by-同舟云学术

Prediction of tunnel mechanical behaviour using multi-task deep learning under the external condition

Published:2023-03-07 Issue: Volume: Page:1-13
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:

Du Bowen¹,Zou Tao¹,Ye Junchen¹,Tan Xuyan²³^ORCID,Cheng Ke¹,Chen Weizhong²³

Affiliation:

1. SKLSDE Lab, Beihang University, Beijing, People’s Republic of China

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

3. 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

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/10.1080/17499518.2023.2182890

Reference36 articles.

1. Underground space utilisation for urban renewal

2. On robust regression analysis as a means of exploring environmental and operational conditions for SHM data

3. Development of load-temporal model to predict the further mechanical behaviors of tunnel structure under various boundary conditions

4. Finding Structure in Time

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