A Bayesian Network framework for probabilistic identification: Application for identification of model parameters in chloride ingress into concrete - Softcover
Synopsis
Chloride ingress into concrete is one of the major causes leading to the degradation of reinforced concrete structures with important damages after 10 to 20 years. Consequently, they should be periodically inspected and repaired to ensure an optimal level of serviceability and safety. Chloride ingress involves a large number of uncertainties related to material properties and exposure conditions. However, it is difficult to obtain sufficient inspection data to characterise the mid- and long-term behaviour of this phenomenon. The main objective of this thesis is to develop a framework based on Bayesian Network updating for improving the identification of uncertainties related to material and environmental model parameters. Based on results coming from in-lab normal and accelerated tests that simulate tidal conditions, several procedures are proposed to: (1) identify input random variables from normal or natural tests; (2) determine a scale factor for accelerated tests; and (3) characterise time-dependent parameters. The results indicate that the proposed framework could be a useful tool to identify model parameters even from limited data.
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A Bayesian Network framework for probabilistic identification
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LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6205526794
ISBN 13: 9786205526798
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A Bayesian Network framework for probabilistic identification
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A Bayesian Network framework for probabilistic identification
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Chloride ingress into concrete is one of the major causes leading to the degradation of reinforced concrete structures with important damages after 10 to 20 years. Consequently, they should be periodically inspected and repaired to ensure an optimal level of serviceability and safety. Chloride ingress involves a large number of uncertainties related to material properties and exposure conditions. However, it is difficult to obtain sufficient inspection data to characterise the mid- and long-term behaviour of this phenomenon. The main objective of this thesis is to develop a framework based on Bayesian Network updating for improving the identification of uncertainties related to material and environmental model parameters. Based on results coming from in-lab normal and accelerated tests that simulate tidal conditions, several procedures are proposed to: (1) identify input random variables from normal or natural tests; (2) determine a scale factor for accelerated tests; and (3) characterise time-dependent parameters. The results indicate that the proposed framework could be a useful tool to identify model parameters even from limited data. 196 pp. Englisch. Seller Inventory # 9786205526798
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A Bayesian Network framework for probabilistic identification
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A Bayesian Network framework for probabilistic identification
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: TRAN Thanh BinhDr. Thanh Binh TRAN is currently a lecturer of Civil Engineering in the Danang University of Technology. His research mainly focus on numerical and probabilistic modelling of the degradation processes and their interac. Seller Inventory # 793819905
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A Bayesian Network framework for probabilistic identification
Published by
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ISBN 10: 6205526794
ISBN 13: 9786205526798
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Chloride ingress into concrete is one of the major causes leading to the degradation of reinforced concrete structures with important damages after 10 to 20 years. Consequently, they should be periodically inspected and repaired to ensure an optimal level of serviceability and safety. Chloride ingress involves a large number of uncertainties related to material properties and exposure conditions. However, it is difficult to obtain sufficient inspection data to characterise the mid- and long-term behaviour of this phenomenon. The main objective of this thesis is to develop a framework based on Bayesian Network updating for improving the identification of uncertainties related to material and environmental model parameters. Based on results coming from in-lab normal and accelerated tests that simulate tidal conditions, several procedures are proposed to: (1) identify input random variables from normal or natural tests; (2) determine a scale factor for accelerated tests; and (3) characterise time-dependent parameters. The results indicate that the proposed framework could be a useful tool to identify model parameters even from limited data.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 196 pp. Englisch. Seller Inventory # 9786205526798
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A Bayesian Network framework for probabilistic identification | Application for identification of model parameters in chloride ingress into concrete
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Taschenbuch. Condition: Neu. A Bayesian Network framework for probabilistic identification | Application for identification of model parameters in chloride ingress into concrete | Thanh Binh Tran (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786205526798 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 126420919
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A Bayesian Network framework for probabilistic identification : Application for identification of model parameters in chloride ingress into concrete
Published by
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6205526794
ISBN 13: 9786205526798
New
Taschenbuch
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Chloride ingress into concrete is one of the major causes leading to the degradation of reinforced concrete structures with important damages after 10 to 20 years. Consequently, they should be periodically inspected and repaired to ensure an optimal level of serviceability and safety. Chloride ingress involves a large number of uncertainties related to material properties and exposure conditions. However, it is difficult to obtain sufficient inspection data to characterise the mid- and long-term behaviour of this phenomenon. The main objective of this thesis is to develop a framework based on Bayesian Network updating for improving the identification of uncertainties related to material and environmental model parameters. Based on results coming from in-lab normal and accelerated tests that simulate tidal conditions, several procedures are proposed to: (1) identify input random variables from normal or natural tests; (2) determine a scale factor for accelerated tests; and (3) characterise time-dependent parameters. The results indicate that the proposed framework could be a useful tool to identify model parameters even from limited data. Seller Inventory # 9786205526798
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