Bridge fatigue assessment and management using reliability-based crack growth and probability of detection models

Kwon, Kihyon; Frangopol, Dan M.

doi:10.1016/j.probengmech.2011.02.001

Cited by 54 publications

(17 citation statements)

References 18 publications

(28 reference statements)

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“…Other researchers have obtained the same result for simulation of 1 location, e.g. [60]. Intuitively this is in contradiction with the often observed 'bathtub' curve for the probability of fatigue failure in time, where the probability increases as the expected end-of-life approaches.…”

Section: Resultsmentioning

confidence: 62%

Probabilistic fatigue life updating accounting for inspections of multiple critical locations

Maljaars

Vrouwenvelder

2014

International Journal of Fatigue

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Section: Resultsmentioning

confidence: 62%

Probabilistic fatigue life updating accounting for inspections of multiple critical locations

Maljaars

Vrouwenvelder

2014

International Journal of Fatigue

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“…Compared with the calculations of Ref. [34], where the mean of S eq is 0.5CAFT and the variable coefficient is 0.3, it is concluded that the fatigue reliability indices are reasonable. Besides, the difference of the fatigue reliability indices is non-ignorable with S eq in different distributional hypothesis.…”

Section: Linear S−n Curve In Aashtomentioning

confidence: 54%

Hybrid reliability model for fatigue reliability analysis of steel bridges

Cao

Lei

2016

J. Cent. South Univ.

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A kind of hybrid reliability model is presented to solve the fatigue reliability problems of steel bridges. The cumulative damage model is one kind of the models used in fatigue reliability analysis. The parameter characteristics of the model can be described as probabilistic and interval. The two-stage hybrid reliability model is given with a theoretical foundation and a solving algorithm to solve the hybrid reliability problems. The theoretical foundation is established by the consistency relationships of interval reliability model and probability reliability model with normally distributed variables in theory. The solving process is combined with the definition of interval reliability index and the probabilistic algorithm. With the consideration of the parameter characteristics of the S−N curve, the cumulative damage model with hybrid variables is given based on the standards from different countries. Lastly, a case of steel structure in the Neville Island Bridge is analyzed to verify the applicability of the hybrid reliability model in fatigue reliability analysis based on the AASHTO.

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“…Bridge construction can collapse due to construction materials, such as fatique and deterioration. For predicting the deterioration of concrete, ANN (Artificial Neural Network) model can be employed [10], while FRM (Fatique reliability model) can be used for predicting fatique [11]. The relationships between components can also cause bridge construction collapse.…”

Section: Data Collectionsmentioning

confidence: 99%

Bridge Deterioration Prediction Model Based On Hybrid Markov-System Dynamic

2017

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Abstract. Instantaneous bridge failure tends to increase in Indonesia. To mitigate this condition, Indonesia's Bridge Management System (I-BMS) has been applied to continuously monitor the condition of bridges. However, I-BMS only implements visual inspection for maintenance priority of the bridge structure component instead of bridge structure system. This paper proposes a new bridge failure prediction model based on hybrid Markov-System Dynamic (MSD). System dynamic is used to represent the correlation among bridge structure components while Markov chain is used to calculate temporal probability of the bridge failure. Around 235 data of bridges in Indonesia were collected from Directorate of Bridge the Ministry of Public Works and Housing for calculating transition probability of the model. To validate the model, a medium span concrete bridge was used as a case study. The result shows that the proposed model can accurately predict the bridge condition. Besides predicting the probability of the bridge failure, this model can also be used as an early warning system for bridge monitoring activity.

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Bridge fatigue assessment and management using reliability-based crack growth and probability of detection models

Cited by 54 publications

References 18 publications

Probabilistic fatigue life updating accounting for inspections of multiple critical locations

Probabilistic fatigue life updating accounting for inspections of multiple critical locations

Hybrid reliability model for fatigue reliability analysis of steel bridges

Bridge Deterioration Prediction Model Based On Hybrid Markov-System Dynamic

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