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Probabilistic prediction of long-term deformations in concrete structures using Markov Chain Monte Carlo Method

Nicky Reybrouck (UGent) , Pieterjan Criel (UGent) and Robby Caspeele (UGent)
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Abstract
The stress and strain in a reinforced or prestressed concrete structure are subject to change for a long period of time, during which creep and shrinkage of concrete and relaxation of the steel used for prestressing develop gradually. The structural response of a concrete member can be predicted as function of time using calculation models available in literature which incorporate methods to account for these time-dependent effects when the geometry, loading history and environmental conditions are known. However, in case the information regarding the loading history is not available, a good estimation of this unknown loading history is required in order to determine the remaining lifetime of the structure and predict the future time-dependent behaviour. In this contribution a general calculation method is proposed to determine the magnitude, the time of application and their corresponding distributions which describe the unknown loading history when sufficient measurement data of the structural response measured at an arbitrary time interval is provided. The hyperparameters of the hierarchical model which describe the deformations due to the creep behaviour were estimated using the Markov chain Monte Carlo (MCMC) method. An example is given in which the loading history is unknown and the concrete member is showing excessive deflections which might compromise the serviceability of the structure in the future. The loading history was estimated and the entire behaviour of the concrete beam in the past and the future was predicted accurately using a set of measurements of deformations of the concrete member. The proposed method can also be used in other scenarios such as to predict the magnitude of prestressing force and the time of releasing of the strands when the information is unknown and measurement data is available.

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Chicago
Reybrouck, Nicky, Pieterjan Criel, and Robby Caspeele. 2017. “Probabilistic Prediction of Long-term Deformations in Concrete Structures Using Markov Chain Monte Carlo Method.” In 15th International Probabilistic Workshop & 10th Dresdner Probabilistik Workshop, ed. Matthias Voigt, Dirk Proske, Wolfgang Graf, Michael Beer, Ulrich Häussler-Combe, and Paul Voigt, 283–291. Dresden: TUDPress.
APA
Reybrouck, N., Criel, P., & Caspeele, R. (2017). Probabilistic prediction of long-term deformations in concrete structures using Markov Chain Monte Carlo Method. In M. Voigt, D. Proske, W. Graf, M. Beer, U. Häussler-Combe, & P. Voigt (Eds.), 15th International Probabilistic Workshop & 10th Dresdner Probabilistik Workshop (pp. 283–291). Presented at the 15th International Probabilistic Workshop & 10th Dresdner Probabilistik Workshop, Dresden: TUDPress.
Vancouver
1.
Reybrouck N, Criel P, Caspeele R. Probabilistic prediction of long-term deformations in concrete structures using Markov Chain Monte Carlo Method. In: Voigt M, Proske D, Graf W, Beer M, Häussler-Combe U, Voigt P, editors. 15th International Probabilistic Workshop & 10th Dresdner Probabilistik Workshop. Dresden: TUDPress; 2017. p. 283–91.
MLA
Reybrouck, Nicky, Pieterjan Criel, and Robby Caspeele. “Probabilistic Prediction of Long-term Deformations in Concrete Structures Using Markov Chain Monte Carlo Method.” 15th International Probabilistic Workshop & 10th Dresdner Probabilistik Workshop. Ed. Matthias Voigt et al. Dresden: TUDPress, 2017. 283–291. Print.
@inproceedings{8532885,
  abstract     = {The stress and strain in a reinforced or prestressed concrete structure are subject to change for a long period of time, during which creep and shrinkage of concrete and relaxation of the steel used for prestressing develop gradually. The structural response of a concrete member can be predicted as function of time using calculation models available in literature which incorporate methods to account for these time-dependent effects when the geometry, loading history and environmental conditions are known. However, in case the information regarding the loading history is not available, a good estimation of this unknown loading history is required in order to determine the remaining lifetime of the structure and predict the future time-dependent behaviour. In this contribution a general calculation method is proposed to determine the magnitude, the time of application and their corresponding distributions which describe the unknown loading history when sufficient measurement data of the structural response measured at an arbitrary time interval is provided. The hyperparameters of the hierarchical model which describe the deformations due to the creep behaviour were estimated using the Markov chain Monte Carlo (MCMC) method. An example is given in which the loading history is unknown and the concrete member is showing excessive deflections which might compromise the serviceability of the structure in the future. The loading history was estimated and the entire behaviour of the concrete beam in the past and the future was predicted accurately using a set of measurements of deformations of the concrete member. The proposed method can also be used in other scenarios such as to predict the magnitude of prestressing force and the time of releasing of the strands when the information is unknown and measurement data is available.},
  author       = {Reybrouck, Nicky and Criel, Pieterjan and Caspeele, Robby},
  booktitle    = {15th International Probabilistic Workshop \& 10th Dresdner Probabilistik Workshop},
  editor       = {Voigt, Matthias and Proske, Dirk and Graf, Wolfgang and Beer, Michael and H{\"a}ussler-Combe, Ulrich and Voigt, Paul},
  isbn         = {978-3-95908-113-9},
  language     = {eng},
  location     = {Dresden, Germany},
  pages        = {283--291},
  publisher    = {TUDPress},
  title        = {Probabilistic prediction of long-term deformations in concrete structures using Markov Chain Monte Carlo Method},
  year         = {2017},
}