Machine Learning-Based Seismic Reliability Assessment of Bridge Networks
- Authors
- Chen, Mengdie; Mangalathu, Sujith; Jeon, Jong-Su
- Issue Date
- Jul-2022
- Publisher
- ASCE-AMER SOC CIVIL ENGINEERS
- Keywords
- Machine learning models; Bridge network analysis; Network fragility; Bridge ranking; Feature importance
- Citation
- JOURNAL OF STRUCTURAL ENGINEERING, v.148, no.7, pp.1 - 4
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF STRUCTURAL ENGINEERING
- Volume
- 148
- Number
- 7
- Start Page
- 1
- End Page
- 4
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/170044
- DOI
- 10.1061/(ASCE)ST.1943-541X.0003376
- ISSN
- 0733-9445
- Abstract
- Transportation networks are critical components of lifeline systems. They can experience disruptions due to seismic hazards that could lead to severe emergency response and recovery problems. Finding an efficient and effective method to evaluate the seismic reliability of bridge networks is crucial for risk managers. This study proposes a method that can compute the seismic reliability of bridge networks using machine learning techniques. The proposed method is computationally less expensive than existing methods and can be implemented easily in emergency risk management systems. Moreover, it includes information on ranking bridges and prioritizing retrofit plans.
- Files in This Item
-
Go to Link
- Appears in
Collections - 서울 공과대학 > 서울 건설환경공학과 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/170044)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.