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Markov Chain Transition Probability Matrices for Condition Assessment of Concrete Bridge Decks

  • 2025
  • OriginalPaper
  • Chapter
Published in:
Authors
Ali Chehrazi
Mahesh Pandey
Scott Walbridge
Zoubir Lounis
Published in
Proceedings of the Canadian Society for Civil Engineering Annual Conference 2024, Volume 13
Publisher
Springer Nature Switzerland

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Abstract

This chapter delves into the application of Markov Chain models for assessing the condition of concrete bridge decks, with a particular focus on transition probability matrices. The study addresses the challenges of using real inspection data and employs the maximum-likelihood approach to determine these probabilities accurately. It compares the deterioration of concrete bridge decks in cold and warm US states, revealing that decks in colder climates reach poor condition faster than those in warmer climates. The results highlight the importance of considering climatic factors in bridge management and maintenance planning. The chapter also discusses the assumptions and limitations of the Markov Chain model, providing a comprehensive overview of its utility in predicting bridge deck conditions. The findings offer valuable insights for bridge owners, companies, and parties responsible for bridge management, aiding in the development of effective strategies for ensuring bridge safety and longevity.
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previous chapter Development of a Hybrid Hold-Down Joint for Cross-Laminated Timber Balloon-Type Shear Walls Equipped with Self-Centering Seismic Dampers
next chapter Enhancing Seismic Stability in Steel Buildings: A Numerical Evaluation of the Novel Seismic Stability Provisions Proposed for the CSA S16:24 Commentary for Stable and Resilient Post-earthquake Performance
Title
Markov Chain Transition Probability Matrices for Condition Assessment of Concrete Bridge Decks
Authors
Ali Chehrazi
Mahesh Pandey
Scott Walbridge
Zoubir Lounis
Copyright Year
2025
Book
Proceedings of the Canadian Society for Civil Engineering Annual Conference 2024, Volume 13

Print ISBN: 978-3-031-96762-7

Electronic ISBN: 978-3-031-96763-4

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-3-031-96763-4_24
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