A predictive Hidden semi-Markov Model for bridges subject to chloride-induced deterioration

Chloride-induced deterioration is one of the main deterioration mechanisms for bridges. Directly detecting the chloride ion concentration is uneconomical for most areas. Experienced workers estimate the chloride-induced deterioration through the corrosion of reinforced concrete, which results in ine...

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Veröffentlicht in:IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) (Online) S. 751 - 756
Hauptverfasser: Guo, Chunhui, Liang, Zhenglin, Zeng, Junqi, Song, Minyuan, Xue, Zongqi
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2021
Schlagworte:
Benchmark testing
Bridges
chloride-induced deterioration
dectection interval
Estimation
forward-backward algorithm
Hidden Markov models
Hidden semi-Markov Model
Ions
Prediction algorithms
Predictive models
ISSN:2693-9371
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Zusammenfassung:Chloride-induced deterioration is one of the main deterioration mechanisms for bridges. Directly detecting the chloride ion concentration is uneconomical for most areas. Experienced workers estimate the chloride-induced deterioration through the corrosion of reinforced concrete, which results in ineffective maintenance due to the inaccurate estimation. Hence, we formulate a predictive Hidden semi-Markov Model for bridges subject to chloride-induced deterioration. The Hidden semi-Markov Models are capable of handling the "hidden states" scenario and have more general applications than the Hidden Markov Models. We derive the corresponding forward-backward algorithm of the Hidden semi-Markov Model to predict the future deterioration state based on the past observation sequences. A numerical example of reinforced concrete bridge decks is utilized to illustrate the applicability of the overall approach. In the numerical example, we discover that the predictive results of the Hidden semi-Markov Models outperform those of the Hidden Markov Models when we regard the deterioration trend from the physical model-Fick's second law as the benchmark. When the detection intervals are stochastic, the Hidden semi-Markov model is more practical than the Hidden Markov model.
ISSN:2693-9371
DOI:10.1109/QRS-C55045.2021.00114