TY - JOUR UR - http://lib.ugent.be/catalog/pug01:8546080 ID - pug01:8546080 LA - eng TI - Uncertainty quantification of creep in concrete by Taylor expansion PY - 2017 JO - (2017) ENGINEERING STRUCTURES SN - 0141-0296 PB - Elsevier BV 2017 AU - Criel, Pieterjan TW14 000070294482 802001265884 0000-0002-1038-2642 AU - Reybrouck, Nicky TW14 000110714988 AU - Caspeele, Robby TW14 002001067358 802000019840 0000-0003-4074-7478 AU - Matthys, Stijn TW14 801000980060 0000-0002-9588-8561 AB - If deterministic creep prediction models are compared with actual measurement data, often significantdifferences can be observed. These inconsistencies are associated with different causes, i.e. model uncertainty,uncertain input parameters, measurement errors and wrongfully applying creep prediction modelsoutside their limitations. First, the physical mechanism causing creep of concrete is not yet fullyunderstood. Therefore, it is very likely that certain influences on creep of concrete are not consideredin these prediction models, resulting in systematic model errors. The model errors can be quantifiedby comparing prediction results with experimental data. Secondly, the stochastic character of the inputparameters form an additional source of uncertainty which can be quantified by the variance of themodel response. The coefficient of variation in function of time-duration, i.e. the time since the applicationof the load, is a useful measure to quantify the level of uncertainty. In the literature, statistical analysisby means of numerical simulations are often used for this matter. However, even for specializedsampling techniques, a large amount of samples is necessary to cover the relevant ranges of various inputparameters. The aim of the present study is to provide an approximate uncertainty quantification of thecreep prediction models given uncertain input parameters. This approximation is based on a Taylor seriesapproach. This approach has the advantage that is does not require numerical simulations nor does itrequire the knowledge of the probability density function of the input parameters. This method is evaluatedand compared with the statistical analysis for several creep prediction models available in literatureand design codes. ER -Download RIS file
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022 | a 0141-0296 | ||
024 | a 000417658400025 2 wos | ||
024 | a 1854/LU-8546080 2 handle | ||
024 | a 10.1016/j.engstruct.2017.10.004 2 doi | ||
040 | a UGent | ||
245 | a Uncertainty quantification of creep in concrete by Taylor expansion | ||
260 | b Elsevier BV c 2017 | ||
520 | a If deterministic creep prediction models are compared with actual measurement data, often significantdifferences can be observed. These inconsistencies are associated with different causes, i.e. model uncertainty,uncertain input parameters, measurement errors and wrongfully applying creep prediction modelsoutside their limitations. First, the physical mechanism causing creep of concrete is not yet fullyunderstood. Therefore, it is very likely that certain influences on creep of concrete are not consideredin these prediction models, resulting in systematic model errors. The model errors can be quantifiedby comparing prediction results with experimental data. Secondly, the stochastic character of the inputparameters form an additional source of uncertainty which can be quantified by the variance of themodel response. The coefficient of variation in function of time-duration, i.e. the time since the applicationof the load, is a useful measure to quantify the level of uncertainty. In the literature, statistical analysisby means of numerical simulations are often used for this matter. However, even for specializedsampling techniques, a large amount of samples is necessary to cover the relevant ranges of various inputparameters. The aim of the present study is to provide an approximate uncertainty quantification of thecreep prediction models given uncertain input parameters. This approximation is based on a Taylor seriesapproach. This approach has the advantage that is does not require numerical simulations nor does itrequire the knowledge of the probability density function of the input parameters. This method is evaluatedand compared with the statistical analysis for several creep prediction models available in literatureand design codes. | ||
598 | a A1 | ||
100 | a Criel, Pieterjan u TW14 0 000070294482 0 802001265884 0 0000-0002-1038-2642 9 07508CAE-F0EE-11E1-A9DE-61C894A0A6B4 | ||
700 | a Reybrouck, Nicky u TW14 0 000110714988 0 802001742396 9 3A71EBF0-F0EE-11E1-A9DE-61C894A0A6B4 | ||
700 | a Caspeele, Robby u TW14 0 002001067358 0 802000019840 0 0000-0003-4074-7478 9 F8467408-F0ED-11E1-A9DE-61C894A0A6B4 | ||
700 | a Matthys, Stijn u TW14 0 801000980060 0 0000-0002-9588-8561 9 F4D83FF4-F0ED-11E1-A9DE-61C894A0A6B4 | ||
650 | a Technology and Engineering | ||
653 | a Concrete | ||
653 | a creep | ||
653 | a model | ||
653 | a design | ||
773 | t ENGINEERING STRUCTURES g ENGINEERING STRUCTURES. 2017. Elsevier BV. 153 p.334-341 q 153:<334 | ||
856 | 3 Full Text u https://biblio.ugent.be/publication/8546080/file/8546081 z [ugent] y Pieterjan1-s2.0-S0141029616312822-main.pdf | ||
920 | a article | ||
Z30 | x EA 1 TW14 | ||
922 | a UGENT-EA |
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