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Good agreement between model and test results is found.
The determined stress relaxation parameters are used in the existing experimental results of shrinkage stress tests that are independently carried out and reasonable agreement between model and test data is found.
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The comparison study between model simulation and test results indicates that the internal-frost damage model can reasonably predict the crack nucleation and propagation within multiphase cement microstructure.
The results of the complete rail vehicle field tests, show remarkable agreement between proposed model and test data.
The consistence of the growth trend between the model and test results indicates the feasibility and reliability of the coupled model.
Because of differences in the results of two approaches, the finite element (FE) model is updated based on the genetic algorithm (GA) by minimizing the differences between analytical model and test results.
The comparison illustrates a good correlation between model prediction and test results, and a clearly improved performance in terms of accuracy and precision is achieved.
In Table 4 and Fig. 6, comparisons between model predictions and test results for flexural load capacity and unbonded tendon strain are presented.
As the details of tested prestressed beams with unbonded CFRP tendons have been reported elsewhere (Heo et al. 2013), the discussion herein is focused primarily on comparisons between model predictions and test results on flexural capacity and at the ultimate state of the tested beams.
However, we also found that for strong mismatches of training and test data (in the RealData scenario), our SE algorithm is still capable of improving ASR performance with DNNs, presumably since it alleviates the train-test-mismatch and provides a better match between the trained model and test observations.
JSim provides a graph of residuals (the differences between model and data); sign tests and other statistical appraisals of the residuals as a function of time help to distinguish systematic from random deviations.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com