Application of Computer Network Technology (NT) in Transportation Infrastructure Management System (TIMS)

Chen, Zhang; Sun, L.

doi:10.1061/40730(144)98

Cited by 2 publications

(4 citation statements)

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“…For example, the existing deterioration model in the BMS of Shanghai uses a deterministic regression equation, as shown in Equation (1). Deterministic models have the advantages of simple modeling and convenient correcting [12], but it is quite challenging to reflect the uncertainty and randomness of the deterioration process. Besides, the requirement of high-quality historical data is usually hard to be satisfied, and the preprocessing of original data may also cause a risk of subjective judgment [13].…”

Section: Summary Of Basic Theorymentioning

confidence: 99%

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Developing A Semi-Markov Process Model for Bridge Deterioration Prediction in Shanghai

Fang

Sun

2019

Sustainability

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The performance of urban bridges will deteriorate gradually throughout service life. Bridge deterioration prediction is essential for bridge management, especially for maintenance planning and decision-making. By considering the time-dependent reliability in the bridge deterioration process, a Weibull distribution based semi-Markov process model for urban bridge deterioration prediction was proposed in this paper. Historical inspection records stored in the Bridge Manage System (BMS) database in Shanghai since 2004 were investigated. The Weibull distribution was used to characterize the bridge deterioration behavior within each condition rating (CR), and the semi-Markov process was used to calculate the bridge transition probabilities between adjacent CRs. After that, the service life expectancy of urban bridges, the transition probabilities of the deck system and the substructure, and the future CR proportion change caused by deterioration was predicted. The prediction results indicate that the life expectancy of concrete beam bridges is about 77 years. The decay rate of the deck system is the fastest among three major parts, and the substructure has a much longer life expectancy. It suggests that the overall prediction accuracy of the semi-Markov model in network-level is better than the regression analysis method. Furthermore, the proportion of bridges in intact condition will gradually decrease in the next few decades, while the percentage of bridges in the qualified and bad state will increase rapidly. The prediction results show a good agreement with the actual deterioration trend of the urban bridges in Shanghai. In order to alleviate the pressure of bridge maintenance in the future, it is necessary to adopt a more targeted preventive maintenance strategy.

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Section: Summary Of Basic Theorymentioning

confidence: 99%

“…where Q ij (t) is the semi-Markov kernel, and X n is the successive state visited of the n th transition. As well known, the eventual transition probability p e ij shown as Equation (12) represents the probability that the process can move from the state i to the state j neglecting the duration T i in the state i. p e ij = lim…”

Section: Semi-markov Processmentioning

confidence: 99%

Developing A Semi-Markov Process Model for Bridge Deterioration Prediction in Shanghai

Fang

Sun

2019

Sustainability

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“…Two categories of methods can be used to develop bridge deterioration models, deterministic and stochastic methods. The deterministic model assumes that the bridge deterioration tendency is certain, so regression analysis is commonly used to determine the decay rate [2][3][4]. These models are often relatively convenient to use but fail to consider uncertainty and randomness of the bridge deterioration process [5].…”

Section: Introductionmentioning

confidence: 99%

“…Model Assumption and Matrices Simplification.The CR in Shanghai BMS has five grades which can generate the state vector space of Markov-chain model. For ease of calculation, the state vector should be transformed from [A, B, C, D, E] to[5,4,3,2,1]…”

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confidence: 99%

Deterioration Prediction of Urban Bridges on Network Level Using Markov-Chain Model

Sun

Ning

2014

Mathematical Problems in Engineering

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Bridges play an important role in urban transportation network. However, it is hard to predict the bridge deterioration precisely in Shanghai, because records of various bridge types with different maintenance status coexist in the same database and the bridge age span is also large. Therefore a Markov-chain model capable of considering maintenance factors is proposed in this study. Three deterioration circumstances are modeled including natural decay, conventional recoverable decay, and enhanced recoverable decay. Three components as well as the whole bridge are predicted including bridge deck system, superstructure, and substructure. The Markov-chain model proposed can predict not only the distribution of the percentage of different condition rating (CR) grades on network level in any year but also the deterioration tendency of single bridge with any state. Bridge data records of ten years were used to verify the model and also to find the deterioration tendency of urban bridges in Shanghai. The results show that the bridge conditions would drop rapidly if no recoverable repair treatments were conducted. Proper repair could slow down the deterioration speed. The enhanced recoverable repair could significantly mitigate the deterioration process and even raise the CR grades after several years of maintenance and repair.

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Application of Computer Network Technology (NT) in Transportation Infrastructure Management System (TIMS)

Cited by 2 publications

References 0 publications

Developing A Semi-Markov Process Model for Bridge Deterioration Prediction in Shanghai

Developing A Semi-Markov Process Model for Bridge Deterioration Prediction in Shanghai

Deterioration Prediction of Urban Bridges on Network Level Using Markov-Chain Model

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