A hybrid physical data informed DNN in axial displacement prediction of immersed tunnel joint-Reference-Cited by-同舟云学术

A hybrid physical data informed DNN in axial displacement prediction of immersed tunnel joint

Published:2023-01-02 Issue:1 Volume:17 Page:169-180
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:

Yan Wei¹²,Yan Yu³,Shen Ping¹,Zhou Wan-Huan¹²

Affiliation:

1. State Key Laboratory of Internet of Things for Smart City, Department of Civil and Environmental Engineering, University of Macau, Macau, People’s Republic of China

2. Zhuhai UM Science & Technology Research Institute, Zhuhai, People’s Republic of China

3. Hong Kong-Zhuhai-Macao Bridge Authority, Zhuhai, People’s Republic of China

Funder

Ministry of Science and Technology (MOST) of Taiwan, R.O.C

Guangdong Provincial Department of Science and Technology

the Science and Technology Development Fund, Macao SAR

SKL-IOTSC

Funds for International Cooperation and Exchange of the National Natural Science Foundation of China

Hong Kong-Zhuhai-Macao Bridge National Field Scientific Observation

University of Macau

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/10.1080/17499518.2023.2169941

Reference38 articles.

1. Design, construction and monitoring of an underwater retaining wall close to an existing immersed tunnel

2. Physics-guided machine learning for improved accuracy of the National Solar Radiation Database

3. XGBoost

4. Probabilistic physics-guided machine learning for fatigue data analysis

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