| Summary: | The polyacrylamide (PAAm) hydrogel, a transparent and nearly elastic material, has applications
in diverse fields such as biomedical, agricultural, and water treatment. While extensive research
has explored its many mechanical properties, a critical gap exists in understanding the impact of
dehydration on PAAm hydrogel fatigue fracture. This is crucial due to PAAm hydrogel’s
susceptibility to dehydration given its rich-water structure. Therefore, this project aims to
investigate the fatigue fracture of PAAm hydrogel under dehydration, focusing specifically on
fatigue life. Machine learning (ML) models will be employed to predict the fatigue life, addressing
the limitations of existing mathematical models.
The study begins with a pure shear test of PAAm hydrogel under dehydration, gathering 90 raw
data samples with variations in loading rates (200/250/300 mm/min) and maximum displacement
(20/25/30 mm). A supplementary experiment was conducted to compare fatigue fracture behaviour
under both dehydration and constant moisture conditions using 10 samples. The 90 raw data
samples were processed using MATLAB to generate graphs for fatigue life determination and
hysteresis loss analysis. Feature extraction was also performed using MATLAB, followed by data
cleaning and statistical analysis using Jupyter Notebook. Subsequently, augmented data was
generated from the original data and integrated with it to create a new dataset through the data
augmentation process. Both the original and new datasets were separately fitted with 5 ML models
to predict fatigue life under dehydration.
The study revealed that dehydration significantly alters fatigue fracture behaviour, leading to an
abrupt and extensive fracture at fatigue life and a transition to an opaque state. The fatigue life
generally tends to increase with loading frequency and decrease with maximum stretch. Hysteresis
loss was also found to be negligible during fatigue. Moreover, the KNN model was identified as
the most suitable ML model, achieving a test score of 0.68 for the original dataset and 0.90 for the
new dataset.
These findings contribute to a better understanding and highlight the adverse effects of dehydration
on the fatigue fracture of PAAm hydrogel. They also demonstrate the potential of ML models in
predicting the mechanical properties of hydrogels for future studies.
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