Prediction Model for Subway Tunnel Collapse Risk Based on Delphi-Ideal Point Method and Geological Forecast


Дәйексөз келтіру

Толық мәтін

Ашық рұқсат Ашық рұқсат
Рұқсат жабық Рұқсат берілді
Рұқсат жабық Тек жазылушылар үшін

Аннотация

A risk assessment is an effective means of identifying and preventing potential tunnel collapses during construction. The longitudinal wave velocity, burial depth of the tunnel, tunnel span, surroundings, groundwater, and construction factors are selected to build a comprehensive prediction system. The weight of each index is calculated based on the Delphi method. Finally, the risk level for each tunnel section is determined using the ideal point theory. The established prediction model is applied to an actual project to verify its correctness, and the prediction results have good consistency with the actual tunnel. This paper provides a new method for assessing the risk of collapse in subway tunnels.

Авторлар туралы

Yiguo Xue

Research Center of Geotechnical and Structural Engineering, Shandong University

Email: qdh2011@126.com
ҚХР, Jinan, Shandong

Zhiqiang Li

Research Center of Geotechnical and Structural Engineering, Shandong University

Email: qdh2011@126.com
ҚХР, Jinan, Shandong

Daohong Qiu

Research Center of Geotechnical and Structural Engineering, Shandong University

Хат алмасуға жауапты Автор.
Email: qdh2011@126.com
ҚХР, Jinan, Shandong

Weimin Yang

Research Center of Geotechnical and Structural Engineering, Shandong University

Email: qdh2011@126.com
ҚХР, Jinan, Shandong

Lewen Zhang

Institute of Marine Science and Technology, Shandong University

Email: qdh2011@126.com
ҚХР, Qingdao, Shandong

Yufan Tao

Research Center of Geotechnical and Structural Engineering, Shandong University

Email: qdh2011@126.com
ҚХР, Jinan, Shandong

Kai Zhang

Research Center of Geotechnical and Structural Engineering, Shandong University

Email: qdh2011@126.com
ҚХР, Jinan, Shandong

Қосымша файлдар

Қосымша файлдар
Әрекет
1. JATS XML

© Springer Science+Business Media, LLC, part of Springer Nature, 2019