A Probability-Based Likelihood Function for Bayesian Updating of a Bridge Condition Deterioration Model-Reference-Cited by-同舟云学术

A Probability-Based Likelihood Function for Bayesian Updating of a Bridge Condition Deterioration Model

Published:2024-08 Issue:8 Volume:29 Page:
ISSN:1084-0702
Container-title:Journal of Bridge Engineering
language:en
Short-container-title:J. Bridge Eng.

Author:

Zhang Yaotian¹,Li Quanwang²^ORCID,Zhang Hao³

Affiliation:

1. Graduate Student, Dept. of Civil Engineering, Tsinghua Univ., Beijing 100084, China.

2. Associate Professor, Dept. of Civil Engineering, Tsinghua Univ., Beijing 100084, China (corresponding author). ORCID: .

3. Associate Professor, School of Civil Engineering, Univ. of Sydney, Sydney 2006, Australia.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/JBENF2.BEENG-6477

Reference28 articles.

1. Deterioration Rates of Typical Bridge Elements in New York

2. Ali, G., A. Elsayegh, R. Assaad, and I. H. El-Adaway. 2020. “Deck, superstructure, and substructure deterioration prediction for bridges using deep artificial neural networks.” In Proc., Paper Presented at the Construction Research Congress (CRC) on Construction Research and Innovation to Transform Society. Reston, VA: ASCE.

3. Data-driven prediction of long-term deterioration of RC bridges

4. Bayesian Finite Element Model Updating and Assessment of Cable-Stayed Bridges Using Wireless Sensor Data

5. Bridge rating using in-service data in the presence of strength deterioration and correlation in load processes

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