Machine learning-driven feature importance appraisal of seismic parameters on tunnel damage and seismic fragility prediction-Reference-Cited by-同舟云学术

Machine learning-driven feature importance appraisal of seismic parameters on tunnel damage and seismic fragility prediction

Published:2024-11 Issue: Volume:137 Page:109101
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Wang Qi,Geng Ping,Wang Liangjie,He Dingwei,Shen Huoming

Publisher

Elsevier BV

Reference70 articles.

1. Use of fragility curves to assess the seismic vulnerability in the risk analysis of mountain tunnels;Andreotti;Tunn. Undergr. Space Technol.,2019

2. Seismic fragility curves of shallow tunnels in alluvial deposits;Argyroudis;Soil Dynam. Earthq. Eng.,2012

3. Seismic response and performance prediction of steel buckling-restrained braced frames using machine-learning methods;Asgarkhani;Eng. Appl. Artif. Intell.,2024

4. System identification method based on interpretable machine learning for unknown aircraft dynamics;Cao;Aero. Sci. Technol.,2022

5. Assessing FEMA HAZUS-MH MR3: constraining seismic hazard estimates for riverside county using precariously balanced rocks and seismic amplification data;Cochran;Bull. Seismol. Soc. Am.,1997

Cited by 2 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. Study on the damage cause of tunnel junction under different component earthquakes based on on-site investigation and numerical simulation;Soil Dynamics and Earthquake Engineering;2024-11

2. Seismic damage to non-fault-crossing and fault-crossing tunnels: Comparative study of the 2008 Wenchuan earthquake (Mw 7.9) and the 2022 Menyuan earthquake (Mw 6.7);Engineering Failure Analysis;2024-09