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General Information
    • ISSN: 2010-3700
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Engineering & Technology Digital Library, Google Scholar, Crossref, ProQuest, Electronic Journals Library, DOAJ and EI (INSPEC, IET).
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(4): 313-318 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.526

A Study of Machine Learning Techniques for Detecting and Classifying Structural Damage

William Nick, Kassahun Asamene, Gina Bullock, Albert Esterline, and Mannur Sundaresan
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).

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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.

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