Abstract—We report on work that is part of the development
of an agent-based structural health monitoring system. The
data used are acoustic emission signals, and we classify these
signals according to source mechanisms, those associated with
crack growth being particularly significant. The agents are
proxies for communication- and computation-intensive
techniques and respond to the situation at hand by determining
an appropriate constellation of techniques. It is critical that the
system have a repertoire of classifiers with different
characteristics so that a combination appropriate for the
situation at hand can generally be found. We use unsupervised
learning for identifying the existence and location of damage
but supervised learning for identifying the type and severity of
damage. The supervised learning techniques investigated are
support vector machines (SVM), naive Bayes classifiers, and
feed-forward neural networks (FNN). The unsupervised
learning techniques investigated are k-means (with k equal to 3,
4, 5, and 6) and self-organizing maps (SOM, with 3, 4, 5, and 6
output neurons). For each technique except SOM, we tested
versions with and without principal component analysis (PCA)
to reduce the dimensionality of the data. We found significant
differences in the characteristics of these machine learning
techniques, with trade-offs between accuracy and fast
classification runtime that can be exploited by the agents in
finding appropriate combinations of classification techniques.
The approach followed here can be generalized for exploring
the characteristics of machine-learning techniques for
monitoring various kinds of structures.
Index Terms—Machine learning, multiagent systems,
structural health monitoring.
The authors are with the Department of Computer Science, North
Carolina A&T State University, Greensboro, NC 27411, USA (e-mail:
wmnick@aggies.ncat.edu, kass842@yahoo.com, ginabull@hotmail.com,
esterlin@ncat.edu, mannur@ncat.edu).
Cite: William Nick, Kassahun Asamene, Gina Bullock, Albert Esterline, and Mannur Sundaresan, "A Study of Machine Learning Techniques for Detecting and Classifying Structural Damage," International Journal of Machine Learning and Computing vol. 5, no. 4, pp. 313-318, 2015.