Performance Prediction for Steel Bridges Using SHM Data and Bayesian Dynamic Regression Linear Model: A Novel Approach-Reference-Cited by-同舟云学术

Performance Prediction for Steel Bridges Using SHM Data and Bayesian Dynamic Regression Linear Model: A Novel Approach

Published:2024-07 Issue:7 Volume:29 Page:
ISSN:1084-0702
Container-title:Journal of Bridge Engineering
language:en
Short-container-title:J. Bridge Eng.

Author:

Qu Guang¹^ORCID,Sun Limin²^ORCID

Affiliation:

1. Ph.D. Candidate, Dept. of Bridge Engineering, School of Civil Engineering, Tongji Univ., 1239 Siping Rd., Shanghai 200092, China. ORCID: .

2. Professor, State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji Univ., 1239 Siping Rd., Shanghai 200092, China; Shanghai Qi Zhi Institute, Yunjing Rd. 701, Xuhui, Shanghai 200232, China (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JBENF2.BEENG-6435

Reference48 articles.

1. A Bayesian generalised extreme value model to estimate real-time pedestrian crash risks at signalised intersections using artificial intelligence-based video analytics;Ali Y.;Anal. Methods Accid. Res.,2023

2. Strain prediction of bridge SHM based on CEEMDAN-ARIMA model;Bian S.;IOP Conf. Ser.: Earth Environ. Sci.,2020

3. Generalized bridge network performance analysis with correlation and time-variant reliability

4. Dynamic Warning Method for Structural Health Monitoring Data Based on ARIMA: Case Study of Hong Kong–Zhuhai–Macao Bridge Immersed Tunnel

5. Condition monitoring of bridges with non-contact testing technologies

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