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Uncertainty quantification of creep in concrete by Taylor series expansion

Pieterjan Criel (UGent) , Robby Caspeele (UGent) , Stijn Matthys (UGent) and Luc Taerwe (UGent)
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Abstract
If deterministic creep prediction models are compared with actual measurements data, often significant differences can be observed. These inconsistencies are associated to various uncertainties. First, the physical mechanism causing creep of concrete is not yet fully understood. Hence, uncertainty of prediction models can be attributed to an insufficient description of the physical mechanism causing creep. it is very likely that certain influences of creep of concrete are not fully considered in current prediction models, resulting in systematic model errors. Because this error is due to a lack of understanding of the underlying physical mechanisms it can only be quantified by comparing prediction results with experimental data. Secondly, the stochastic character of the input parameters form an additional source of uncertainty which can be quantified by the variance of the model response. The coëfficiënt of variation in function of time-duration is a useful measure to quantify the level of uncertainty due to the stochastic nature of the input parameters. In literature statistical analysis by means of numerical simulations are often used for this matter. However, even for specialized sampling techniques, a large amount of samples is necessary to cover the relevant ranges of various input parameters. The aim of the present study is to provide an approximate uncertainty quantification based on a Taylor series approach. Such method has the advantage that it does not require sampling nor the knowledge of the probability density function of the input parameters. This approximate method to quantify the uncertainty due to the input parameters is evaluated and compared with the statistical analysis for several creep prediction models available in literature and design codes.
Keywords
Creep, concrete, Taylor series, uncertainty quantification

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Chicago
Criel, Pieterjan, Robby Caspeele, Stijn Matthys, and Luc Taerwe. 2016. “Uncertainty Quantification of Creep in Concrete by Taylor Series Expansion.” In 14th International Probabilistic Workshop, ed. Robby Caspeele, Luc Taerwe, and Dirk Proske, 175–188. Springer International Publishing.
APA
Criel, Pieterjan, Caspeele, R., Matthys, S., & Taerwe, L. (2016). Uncertainty quantification of creep in concrete by Taylor series expansion. In R. Caspeele, L. Taerwe, & D. Proske (Eds.), 14th International Probabilistic Workshop (pp. 175–188). Presented at the 14th International Probabilistic Workshop, Springer International Publishing.
Vancouver
1.
Criel P, Caspeele R, Matthys S, Taerwe L. Uncertainty quantification of creep in concrete by Taylor series expansion. In: Caspeele R, Taerwe L, Proske D, editors. 14th International Probabilistic Workshop. Springer International Publishing; 2016. p. 175–88.
MLA
Criel, Pieterjan, Robby Caspeele, Stijn Matthys, et al. “Uncertainty Quantification of Creep in Concrete by Taylor Series Expansion.” 14th International Probabilistic Workshop. Ed. Robby Caspeele, Luc Taerwe, & Dirk Proske. Springer International Publishing, 2016. 175–188. Print.
@inproceedings{8525284,
  abstract     = {If deterministic creep prediction models are compared with actual measurements data, often significant differences can be observed. These inconsistencies are associated to various uncertainties. First, the physical mechanism causing creep of concrete is not yet fully understood. Hence, uncertainty of prediction models can be attributed to an insufficient description of the physical mechanism causing creep. it is very likely that certain influences of creep of concrete are not fully considered in current prediction models, resulting in systematic model errors. Because this error is due to a lack of understanding of the underlying physical mechanisms it can only be quantified by comparing prediction results with experimental data. Secondly, the stochastic character of the input parameters form an additional source of uncertainty which can be quantified by the variance of the model response. The co{\"e}ffici{\"e}nt of variation in function of time-duration is a useful measure to quantify the level of uncertainty due to the stochastic nature of the input parameters. In literature statistical analysis by means of numerical simulations are often used for this matter. However, even for specialized sampling techniques, a large amount of samples is necessary to cover the relevant ranges of various input parameters. The aim of the present study is to provide an approximate uncertainty quantification based on a Taylor series approach. Such method has the advantage that it does not require sampling nor the knowledge of the probability density function of the input parameters. This approximate method to quantify the uncertainty due to the input parameters is evaluated and compared with the statistical analysis for several creep prediction models available in literature and design codes.},
  author       = {Criel, Pieterjan and Caspeele, Robby and Matthys, Stijn and Taerwe, Luc},
  booktitle    = {14th International Probabilistic Workshop},
  editor       = {Caspeele, Robby and Taerwe, Luc and Proske, Dirk},
  isbn         = {978-3-319-47885-2},
  language     = {eng},
  location     = {Ghent (Belgium)},
  pages        = {175--188},
  publisher    = {Springer International Publishing},
  title        = {Uncertainty quantification of creep in concrete by Taylor series expansion},
  url          = {http://dx.doi.org/10.1007/978-3-319-47886-9},
  year         = {2016},
}

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