This dissertation investigates the level of acid‐resistance of concrete degradation.
Concrete specimens obtained from four mixtures (M1, M2, M3 and M4) were
prepared with calcareous, siliceous and a blend of calcareous and silica sand; and
then, tested in low (30 g/l) and highly (200 g/l) concentrated sulphuric acid solutions.
To this end, an architecture of artificial neural networks (ANNs) was implemented to
predict the performance of concrete specimens due to sulphuric acid solutions.
Neural networks were composed with one hidden layer for one input and output
layer. Nine input parameters were: cement composition, proportions of coarse and
fine aggregates, water content, and compressive strength, weight loss of concrete,
time impacting corrosion, acid concentration and sulphur concentration. Thickness
expansion and concrete conductivity are used as output targets to evaluate the
degree of deterioration.
In this study, the learning through ANNs from training data sets have been proved to
be better than measured data. Excellent results were found with a coefficient of
determination (R2
) of 0.9989, 0.9999, 0.9989 and 0.9998, respectively for the four
mixtures M1, M2, M3 and M4 using siliceous aggregate. Also, the results show that
two ANN models performed with both the thickness (expansion) and the electrical
conductivity can successfully learn the prediction of concrete corrosion. In both low
and highly concentrated sulphuric acid condition, the model thickness was more
accurate in predicting concrete corrosion compared to the model conductivity. The
lowest error in neural networks was provided by the mixture (M2) for the concrete
using siliceous aggregate. For this purpose, the root mean squared error (RMSE) and
the average absolute error (AAE) were of 0.0049 and 0.0048 % respectively. / College of Engineering, Science and Technology / M. Tech. (Chemical Engineering)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:uir.unisa.ac.za:10500/26698 |
| Date | 10 1900 |
| Creators | Mutunda, Andre |
| Contributors | Mulenga, Francois |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | 1 online resource (xvii, 147 leaves) : illustrations (chiefly color), graphs (chiefly color, color maps, application/pdf |
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