• Title/Summary/Keyword: 모델 업데이팅

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Durability Prediction for Concrete Structures Exposed to Chloride Attack Using a Bayesian Approach (베이지안 기법을 이용한 염해 콘크리트구조물의 내구성 예측)

  • Jung, Hyun-Jun;Zi, Goang-Seup;Kong, Jung-Sik;Kang, Jin-Gu
    • Journal of the Korea Concrete Institute
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    • v.20 no.1
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    • pp.77-88
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    • 2008
  • This paper provides a new approach for predicting the corrosion resistivity of reinforced concrete structures exposed to chloride attack. In this method, the prediction can be updated successively by a Bayesian theory when additional data are available. The stochastic properties of model parameters are explicitly taken into account into the model. To simplify the procedure of the model, the probability of the durability limit is determined from the samples obtained from the Latin hypercube sampling technique. The new method may be very useful in designing important concrete structures and help to predict the remaining service life of existing concrete structures which have been monitored.

A Long-term Durability Prediction for RC Structures Exposed to Carbonation Using Probabilistic Approach (확률론적 기법을 이용한 탄산화 RC 구조물의 내구성 예측)

  • Jung, Hyun-Jun;Kim, Gyu-Seon
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.14 no.5
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    • pp.119-127
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    • 2010
  • This paper provides a new approach for durability prediction of reinforced concrete structures exposed to carbonation. In this method, the prediction can be updated successively by a Bayes' theorem when additional data are available. The stochastic properties of model parameters are explicitly taken into account in the model. To simplify the procedure of the model, the probability of the durability limit is determined based on the samples obtained from the Latin Hypercube Sampling(LHS) technique. The new method may be very useful in design of important concrete structures and help to predict the remaining service life of existing concrete structures which have been monitored. For using the new method, in which the prior distribution is developed to represent the uncertainties of the carbonation velocity using data of concrete structures(3700 specimens) in Korea and the likelihood function is used to monitor in-situ data. The posterior distribution is obtained by combining a prior distribution and a likelihood function. Efficiency of the LHS technique for simulation was confirmed through a comparison between the LHS and the Monte Calro Simulation(MCS) technique.