  Offshore slender marine structures experience complex and combined load
conditions from waves, current and vessel motions that may result in both wave
frequency and vortex shedding response patterns. Field measurements often
consist of records of environmental conditions and riser responses, typically
with 30-minute intervals. These data can be represented in a high-dimensional
parameter space. However, it is difficult to visualize and understand the
structural responses, as they are affected by many of these parameters. It
becomes easier to identify trends and key parameters if the measurements with
the same characteristics can be grouped together. Cluster analysis is an
unsupervised learning method, which groups the data based on their relative
distance, density of the data space, intervals, or statistical distributions.
In the present study, a Gaussian mixture model guided by domain knowledge has
been applied to analyze field measurements. Using the 242 measurement events of
the Helland-Hansen riser, it is demonstrated that riser responses can be
grouped into 12 clusters by the identification of key environmental parameters.
This results in an improved understanding of complex structure responses.
Furthermore, the cluster results are valuable for evaluating the riser response
prediction accuracy.
