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Numerical modelling predictions of lining system behaviour during construction are comparable with the measurements when the geosynthetics are covered soon after placement, however, where the geosynthetics are left exposed to the sun for an extended period of time, in situ behaviour of the geosynthetics cannot be replicated by the numerical analysis.
Our results show that it is possible to achieve good predictions of line performance by combining phenotypic and genotypic data generated on elite lines.
A design method for the prediction of lining loads should include the decrease of lining loads due to the stress release before lining installation and the increase of lining loads due to development of ground yielding.
It appears this is because prediction accuracy of lines included in the training dataset was always substantially higher than those not included.
The curvature energy is evaluated and compared with predictions of the line tension model.
A priori prediction of the potential effect of rare polymorphisms, however, may improve predictions of inbred line breeding values and combining ability.
Best linear unbiased predictions (BLUPs) of line performance were calculated using mixed model involving trial as a fixed effect and line as a random effect.
The accuracies reported in this study represent prediction of wheat lines using a training set comprising lines with some degree of relatedness to lines in the validation set.
Experimental maize data indicate that prediction of unrelated lines has low accuracy, and that we can predict between families only if they have some degree of relatedness.
With unique features, the proposed non-destructive method is expected to be used for the online monitoring of wire break activities in long term for the condition assessment and performance prediction of PCCP lines.
Prediction accuracies of the "across" prediction approach were relatively low in both cases, whereby prediction of flint lines using a dent training set was more accurate than vice versa.
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