<?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>1692-1798</journal-id>
<journal-title><![CDATA[Iteckne]]></journal-title>
<abbrev-journal-title><![CDATA[Iteckne]]></abbrev-journal-title>
<issn>1692-1798</issn>
<publisher>
<publisher-name><![CDATA[Universidad Santo Tomás]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1692-17982021000100071</article-id>
<article-id pub-id-type="doi">10.15332/iteckne.v18i1.2542</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Importancia de las redes neuronales artificiales en la ingeniería civil: Una revisión sistemática de la literatura]]></article-title>
<article-title xml:lang="en"><![CDATA[Importance of artificial neural networks in civil engineering: A systematic review of the literature]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Valderrama-Purizaca]]></surname>
<given-names><![CDATA[Frank Jesús]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Chávez-Barturen]]></surname>
<given-names><![CDATA[Daniel Armando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Muñoz-Pérez]]></surname>
<given-names><![CDATA[Sócrates Pedro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Tuesta-Monteza]]></surname>
<given-names><![CDATA[Victor]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mejía-Cabrera]]></surname>
<given-names><![CDATA[Heber Ivan]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Señor de Sipán  ]]></institution>
<addr-line><![CDATA[Pimentel ]]></addr-line>
<country>Peru</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Señor de Sipán  ]]></institution>
<addr-line><![CDATA[Pimentel ]]></addr-line>
<country>Peru</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Señor de Sipán  ]]></institution>
<addr-line><![CDATA[Pimentel ]]></addr-line>
<country>Peru</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad Señor de Sipán  ]]></institution>
<addr-line><![CDATA[Pimentel ]]></addr-line>
<country>Peru</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Universidad Señor de Sipán  ]]></institution>
<addr-line><![CDATA[Pimentel ]]></addr-line>
<country>Peru</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<volume>18</volume>
<numero>1</numero>
<fpage>71</fpage>
<lpage>83</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S1692-17982021000100071&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S1692-17982021000100071&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S1692-17982021000100071&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Las redes neuronales artificiales (RNA) tienen un papel relevante en la actualidad, diversas áreas aplican esta técnica por las ventajas que presentan para resolver problemas complejos con muchas restricciones en comparación con los métodos tradicionales, que están quedando desfasados. Se conoce muy poco acerca de esta técnica y su aplicación en distintas ramas de la Ingeniería Civil. Por tal motivo, la presente investigación tiene por objetivo realizar una revisión sistemática de la literatura para identificar el uso de esta técnica y determinar los resultados de la aplicación de modelos de RNA en la ingeniería civil. Se incluyeron un total de 41 artículos científicos que se distribuyen de la siguiente forma: 6 en Scopus, 1 en ScienceDirect, 23 en ProQuest, 7 en Google Académico, 2 en Dialnet, 2 en SciELO. Se encontró que las RNA son utilizadas para predecir o pronosticar variables asociadas a los campos de estudio de la ingeniería civil, se hallaron 8 aplicaciones de RNA para propiedades del concreto, 11 para propiedades del suelo, 5 para análisis sísmico, 9 para hidráulica, 7 para valorización de inmuebles y 1 para diseño de puentes. Así mismo se encontró que el Perceptrón Multicapa es el modelo de RNA más utilizado, logrando en promedio un R2 de 0.99, lo que demuestra ventajas para resolver problemas con precisión, en tiempos más cortos, con datos faltantes en los conjuntos de datos, así como la reducción del factor de error.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Artificial neural networks (ANN) have a relevant role nowadays; several areas apply this technique due to the advantages they have to solve complex problems with many constraints compared to traditional methods, which are becoming outdated. Very little is known about this technique and its application in different branches of Civil Engineering. For this reason, the present research aims to conduct a systematic review of the literature to identify the use of this technique and to determine the results of the application of ANN models in civil engineering. A total of 41 scientific articles were included, distributed as follows: 6 in Scopus, 1 in ScienceDirect, 23 in ProQuest, 7 in Google Scholar, 2 in DialNet, 2 in SciELO. It was found that ANNs are used to predict or forecast variables associated with the fields of study in civil engineering; 8 applications of ANN were found for concrete properties, 11 for soil properties, 5 for seismic analysis, 9 for hydraulics, 7 for real estate valuation and 1 for bridge design. Likewise, it was found that the multilayer Perceptron is the most used ANN model, achieving an average R2 of 0.99, which shows advantages to solve problems with precision, in shorter times, with missing data in the data sets, as well as the reduction of the error factor.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Red neuronal]]></kwd>
<kwd lng="es"><![CDATA[propiedades del concreto]]></kwd>
<kwd lng="es"><![CDATA[mecánica de suelos]]></kwd>
<kwd lng="es"><![CDATA[análisis sísmico]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[modelo RNA]]></kwd>
<kwd lng="en"><![CDATA[Neural Network]]></kwd>
<kwd lng="en"><![CDATA[concrete properties]]></kwd>
<kwd lng="en"><![CDATA[soil mechanics]]></kwd>
<kwd lng="en"><![CDATA[seismic analysis]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[ANN model]]></kwd>
</kwd-group>
</article-meta>
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