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1China Railway Construction Investment Company, China
2Department of Civil Engineering, Tsinghua University, China
*Corresponding author:Quanwang Lib, Department of Civil Engineering, Tsinghua University, Beijing 100084, China
Submission: November 04, 2024;Published: November 12, 2024
Volume3 Issue5
It is important to predict the future condition of bridges for bridge management. Statistical models of time-in-Condition (TC) can estimate the time that a bridge stays in a given condition, and then be used to predict the future condition of the bridge. To address the problem that a large number of bridge inspection data in China has not been fully recorded, a probability-based likelihood (PBL) function has been proposed in existing research to Bayesian update the TC models using incomplete inspection data. However, the existing PBL function contains multiple integrals, which is not easy to calculate and is not conducive to the engineering promotion of the method. In view of this situation, this paper proposes a simplified likelihood function by assuming that TCs follow independent normal distributions, which is an explicit formula that does not contain integral operations, and greatly reduces the calculation cost. Combined with Markov chain model, the formulas for the calculation of condition transition probabilities are derived based on current condition, time in current condition and service time in future. The accuracy of the proposed method is verified through numerical examples, and the effects of data amount, data completeness and data accuracy are explored.
Keywords:Bridge deterioration model; Condition rating; Bayesian updating; Time in condition; Inspection data
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