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Probabilistic identification of the effects of corrosion propagation on reinforced concrete structures via deflection and crack width measurements
https://orcid.org/http://orcid.org/0000-0002-5938-6265
This study presents a probabilistic framework for accurate prediction of the impacts of corrosion propagation on reinforced concrete (RC) structures. The presented framework uses the ensemble Kalman filter (EnKF) coupled with easily acquired measurements of corrosive crack widths and mid-span deflection increases for identifying and calibrating corrosion propagation models. The calibrated models are consequently used to forecast the extent of corrosion propagation in RC structures. To assess the efficacy of the presented framework, data corresponding to the long-term chloride-induced corrosion experiments initiated in 1984 at “Laboratoire des Materiaux et Durabilite des Constructions” (L.M.D.C.) in Toulouse, south-west France are used. The results accentuate the robustness of the presented EnKF approach by being able to identify and calibrate candidate corrosion propagation models capable of predicting, with reasonable accuracy, the experimental measurements of corrosive crack width and mid-span deflection in RC members.
Probabilistic identification of the effects of corrosion propagation on reinforced concrete structures via deflection and crack width measurements
https://orcid.org/http://orcid.org/0000-0002-5938-6265
This study presents a probabilistic framework for accurate prediction of the impacts of corrosion propagation on reinforced concrete (RC) structures. The presented framework uses the ensemble Kalman filter (EnKF) coupled with easily acquired measurements of corrosive crack widths and mid-span deflection increases for identifying and calibrating corrosion propagation models. The calibrated models are consequently used to forecast the extent of corrosion propagation in RC structures. To assess the efficacy of the presented framework, data corresponding to the long-term chloride-induced corrosion experiments initiated in 1984 at “Laboratoire des Materiaux et Durabilite des Constructions” (L.M.D.C.) in Toulouse, south-west France are used. The results accentuate the robustness of the presented EnKF approach by being able to identify and calibrate candidate corrosion propagation models capable of predicting, with reasonable accuracy, the experimental measurements of corrosive crack width and mid-span deflection in RC members.
Probabilistic identification of the effects of corrosion propagation on reinforced concrete structures via deflection and crack width measurements
https://orcid.org/http://orcid.org/0000-0002-5938-6265
This study presents a probabilistic framework for accurate prediction of the impacts of corrosion propagation on reinforced concrete (RC) structures. The presented framework uses the ensemble Kalman filter (EnKF) coupled with easily acquired measurements of corrosive crack widths and mid-span deflection increases for identifying and calibrating corrosion propagation models. The calibrated models are consequently used to forecast the extent of corrosion propagation in RC structures. To assess the efficacy of the presented framework, data corresponding to the long-term chloride-induced corrosion experiments initiated in 1984 at “Laboratoire des Materiaux et Durabilite des Constructions” (L.M.D.C.) in Toulouse, south-west France are used. The results accentuate the robustness of the presented EnKF approach by being able to identify and calibrate candidate corrosion propagation models capable of predicting, with reasonable accuracy, the experimental measurements of corrosive crack width and mid-span deflection in RC members.
Probabilistic identification of the effects of corrosion propagation on reinforced concrete structures via deflection and crack width measurements
Mater Struct
Bichara, Louise (Autor:in) / Saad, George (Autor:in) / Slika, Wael (Autor:in)
17.08.2019
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Corrosion propagation rate , Reinforced concrete , Ensemble Kalman filter , Data assimilation , Uncertainty quantification Engineering , Solid Mechanics , Materials Science, general , Theoretical and Applied Mechanics , Manufacturing, Machines, Tools, Processes , Civil Engineering , Building Materials
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