<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0120-5609</journal-id>
<journal-title><![CDATA[Ingeniería e Investigación]]></journal-title>
<abbrev-journal-title><![CDATA[Ing. Investig.]]></abbrev-journal-title>
<issn>0120-5609</issn>
<publisher>
<publisher-name><![CDATA[Facultad de Ingeniería, Universidad Nacional de Colombia.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0120-56092024000300013</article-id>
<article-id pub-id-type="doi">10.15446/ing.investig.111646</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Electromechanical Impedance-Based Damage Detection Using Machine Learning Approaches]]></article-title>
<article-title xml:lang="es"><![CDATA[Detección de daños basada en impedancia electromecánica mediante métodos de aprendizaje automático]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Carneiro Pereira]]></surname>
<given-names><![CDATA[Paulo Elias]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
<xref ref-type="aff" rid="A a"/>
<xref ref-type="aff" rid="A3"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ferreira de Rezende]]></surname>
<given-names><![CDATA[Stanley Washington]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pereira Barella]]></surname>
<given-names><![CDATA[Bruno]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vieira de Moura Júnior]]></surname>
<given-names><![CDATA[José dos Reis]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Finzi Neto]]></surname>
<given-names><![CDATA[Roberto Mendes]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Federal University of Uberlândia  ]]></institution>
<addr-line><![CDATA[Uberlândia ]]></addr-line>
<country>Brazil</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Federal University of Catalão  ]]></institution>
<addr-line><![CDATA[Catalão ]]></addr-line>
<country>Brazil</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Federal University of Goiás  ]]></institution>
<addr-line><![CDATA[Goiânia ]]></addr-line>
<country>Brazil</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Federal University of Uberlândia  ]]></institution>
<addr-line><![CDATA[Uberlândia ]]></addr-line>
<country>Brazil</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Federal University of Uberlândia  ]]></institution>
<addr-line><![CDATA[Uberlândia ]]></addr-line>
<country>Brazil</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<volume>44</volume>
<numero>3</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0120-56092024000300013&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0120-56092024000300013&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0120-56092024000300013&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Electromechanical impedance-based structural health monitoring has been the subject of extensive research in recent decades. The method's low cost and ability to detect minor structural damages make it an appealing alternative to other non-destructive techniques. Ongoing research on damage detection approaches continues to be a topic of interest in relation to the electromechanical impedance method. This work proposes the use of the K-Means, Decision Tree, and Random Forest algorithms to distinguish between four structural conditions in an aluminum beam. These techniques were applied to raw impedance data and a dataset reduced via principal components analysis. The findings revealed that the compressed dataset improved the accuracy of all models, except for the Random Forest approach, whose accuracy decreased by 2.9%. The K-Means algorithm was most affected by the reduction in dimensionality, with a 105.9% increase in accuracy. The Decision Tree and Random Forest methods yielded outstanding outcomes, comparable or superior to other state-of-the-art approaches. This makes them a compelling choice for detecting damage using electromechanical impedance data, even when using raw data as the input information.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN El monitoreo de la salud estructural basado en la impedancia electromagnetica ha sido objeto de investigacion exhaustiva en las ultimas decadas. El bajo coste del metodo y su capacidad para detectar daños estructurales menores lo convierten en una alternativa atractiva a otras tecnicas no destructivas. La investigacion actual sobre enfoques de deteccion de daños sigue siendo un tema de interes en lo que concierne al metodo de impedancia electromecanica. En este trabajo se propone utilizar los algoritmos K-Means, Decision Tree y Random Forest para diferenciar entre cuatro condiciones estructurales en una viga de aluminio. Estas tecnicas se aplicaron a datos de impedancia en bruto y a un conjunto de datos reducido mediante analisis de componentes principales. Los resultados revelaron que el conjunto de datos comprimido mejoro la precision de todos los modelos, excepto en el caso del metodo Random Forest, cuya precision disminuyo en un 2.9%. El algoritmo K-Means fue el mas afectado por la reduccion de la dimensionalidad, con un aumento del 105.9% en la precision. Los metodos Decision Tree y Random Forest produjeron resultados sobresalientes, comparables o superiores a otros enfoques de vanguardia. Esto los convierte en una opcion convincente para detectar daños a traves de datos de impedancia electromecanica, incluso cuando se utilizan datos en bruto como informacion de entrada.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[electromechanical impedance method]]></kwd>
<kwd lng="en"><![CDATA[K-means algorithm]]></kwd>
<kwd lng="en"><![CDATA[Decision Tree]]></kwd>
<kwd lng="en"><![CDATA[Random Forest]]></kwd>
<kwd lng="en"><![CDATA[structural health monitoring]]></kwd>
<kwd lng="es"><![CDATA[Metodo de impedancia electromecanica]]></kwd>
<kwd lng="es"><![CDATA[Algoritmo K-Medias]]></kwd>
<kwd lng="es"><![CDATA[Arbol de Decision]]></kwd>
<kwd lng="es"><![CDATA[Bosque Aleatorio]]></kwd>
<kwd lng="es"><![CDATA[Control del Estado Estructural]]></kwd>
</kwd-group>
</article-meta>
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<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[S.-X.]]></given-names>
</name>
<name>
<surname><![CDATA[Ni]]></surname>
<given-names><![CDATA[Y.-Q.]]></given-names>
</name>
<name>
<surname><![CDATA[Choy]]></surname>
<given-names><![CDATA[A. W.-H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[EMI-GCN: A hybrid model for real-time monitoring of multiple bolt looseness using electromechanical impedance and graph convolutional networks]]></article-title>
<source><![CDATA[Smart Materials and Structures]]></source>
<year>2021</year>
<volume>30</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>035032</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
