The key element of this work is to demonstrate a strategy for using pattern recognition algorithms to investigate
correlations between feature variables for Structural Health Monitoring (SHM). The task will take advantage
of data from a bridge. An informative chain of artificial intelligence tools will allow an active learning
interaction between the unfolded shapes of the manifold of online data by characterising the physical shape
between variables. In many data mining and machine learning applications, there is a significant supply
of unlabelled data but an important undersupply of labelled data. Semi-supervised active learning, which
combines both labelled and unlabelled data can offer serious access to useful information and may be the
crucial element in successful decision making, regarding the health of structures
