In summary, the mixture model has two free parameters, (f, α), that determine the fraction of uncorrelated points in the observed data and the aspect ratio of the distribution for correlated data.
In between, dip position data increase as aspect ratio becomes larger.
Bonakdari et al. (2008) showed that both models overestimate experimental data when the aspect ratio is small and suggested a new relation.
However, due to the limited nature of the data set regarding aspect ratio, mix designs, aggregate type and content, etc., investigated, the researchers recommend further testing to increase the database of SCC bond test results.
Due to the limited nature of the data set regarding aspect ratio, mix designs, aggregate type and content, etc. investigated, these results cannot be generalized and the researchers recommend further testing to increase the database of SCC test results.
As predicted results are compared with laboratory data, where the aspect ratio of the test object is required to be less than 1 2, a simplified procedure may be applied involving two single integrals only.
A correction was then applied based on empirical crack aspect ratio data.
By carefully training a neural network with data relating section profile, aspect ratio and size to the load carrying capacity, the neural network can be prepared to estimate the best section requirements in new applications.
But they are still susceptible to the prosaic deceptions of data displays such as scale, aspect ratio and placement.In addition, intensive processing for visualisation can actually destroy information.
However significant discrepancy exists between the theoretical prediction and experimental data when both fire site aspect ratio and tunnel height/width ratio are larger than 1.0.
Considering the aspect ratio as input data, two different fitness functions were investigated to select the best one for modeling the mean wall and bed shear stress.
