Applications of machine learning for modelling of ice flexural strength ...
The design of marine vessels and structures operating in regions where ice is present, must consider the loads transferred to the structure upon impact with an ice feature. The flexural strength of ice is an important material property and can have significant impact on the loads transferred to a st...
| Main Author: | |
|---|---|
| Format: | Text |
| Language: | English |
| Published: |
Memorial University of Newfoundland
2024
|
| Subjects: | |
| Online Access: | https://dx.doi.org/10.48336/cty5-5d90 http://research.library.mun.ca/id/eprint/16395 |
| Summary: | The design of marine vessels and structures operating in regions where ice is present, must consider the loads transferred to the structure upon impact with an ice feature. The flexural strength of ice is an important material property and can have significant impact on the loads transferred to a structure. Flexural strength is generally considered to be dependent on the size or scale of the sample (often reported as beam volume), ice temperature and brine volume (in the case of sea ice), however the influence of temperature and beam volume have been debated in the literature. Conventionally flexural strength was often modelled as a constant (i.e. average strength), or was modelled as a single parameter or dual parameter (sea ice only) empirical relationship. Employing an extensive database of flexural strength measurements, with over 2000 freshwater and 2800 sea ice measurements, machine learning (ML) algorithms were utilized to define a relationship between these ice parameters and the measured flexural ... |
|---|