Rapid damage state classification for underground box tunnels using machine learning-Reference-Cited by-同舟云学术

Rapid damage state classification for underground box tunnels using machine learning

Published:2023-10-13 Issue: Volume: Page:1-14
ISSN:1573-2479
Container-title:Structure and Infrastructure Engineering
language:en
Short-container-title:Structure and Infrastructure Engineering

Author:

Nguyen Van-Quang¹²,Nguyen Hoang D.³,Petrone Floriana³⁴,Park Duhee¹

Affiliation:

1. Department of Civil and Environmental Engineering, Hanyang University, Seoul, Korea

2. Department of Civil Engineering, Vinh University, Vinh, Vietnam

3. Department of Civil and Environmental Engineering, University of Nevada, Reno, NV, USA

4. Energy Geosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

Funder

National Research Foundation of Korea

Korea government

Publisher

Informa UK Limited

Subject

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

Link

https://www.tandfonline.com/doi/pdf/10.1080/15732479.2023.2266709

Reference66 articles.

1. ALA. (2001). Seismic fragility formulations for water systems. Part 1—Guideline. Washington, DC: American Society of Civil Engineers (ASCE)–Federal Emergency Management Agency (FEMA).

2. Theme Paper: Earthquake Engineering for Tunnels and Underground Structures. A Case History

3. Seismic fragility curves of shallow tunnels in alluvial deposits

4. Effect of underground tunnel on the ground surface acceleration

5. Bentz, E., & Collins, M. (2000). Response-2000, V. 1.0. 5. Toronto: Toronto University.

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A comprehensive review of resilience of urban metro systems: A perspective from earthquake engineering;Tunnelling and Underground Space Technology;2024-10

2. Rapid resilience assessment framework for mountain tunnels subjected to near-fault seismic ground motions;Soil Dynamics and Earthquake Engineering;2024-07

3. Machine learning-based model for moment capacity prediction and reliability analysis of PSC beams;Structures;2024-04