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

• Published in: IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C) (Online) pp. 751 - 756
• Main Authors: Guo, Chunhui, Liang, Zhenglin, Zeng, Junqi, Song, Minyuan, Xue, Zongqi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2021
• Subjects: 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
• DOI: 10.1109/QRS-C55045.2021.00114

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.