Conference paper Open Access

Prediction of liquefaction damage with artificial neural networks

Paolella Luca; Salvatore Erminio; Spacagna Rose Line; Modoni Giuseppe; Ochmanski Maciej

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  <identifier identifierType="DOI">10.5281/zenodo.3463412</identifier>
      <creatorName>Paolella Luca</creatorName>
      <affiliation>University of Cassino and Southern Lazio</affiliation>
      <creatorName>Salvatore Erminio</creatorName>
      <affiliation>University of Cassino and Southern Lazio</affiliation>
      <creatorName>Spacagna Rose Line</creatorName>
      <affiliation>University of Cassino and Southern Lazio</affiliation>
      <creatorName>Modoni Giuseppe</creatorName>
      <affiliation>University of Cassino and Southern Lazio</affiliation>
      <creatorName>Ochmanski Maciej</creatorName>
      <affiliation>Silesian University of Technology - Gliwice (Poland)</affiliation>
    <title>Prediction of liquefaction damage with artificial neural networks</title>
    <subject>Artificial Neural Networks</subject>
    <date dateType="Issued">2019-06-23</date>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3463411</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 1.0 Generic</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;The survey of the damage occurred on land, buildings and infrastructures&lt;br&gt;
extensively affected by liquefaction, coupled with a comprehensive investigation of the subsoil&lt;br&gt;
properties enables to identify the factors that determine the spatial distribution of the phenomenon.&lt;br&gt;
With this goal, a database was created in a Geographic Information platform merging&lt;br&gt;
records of local seismicity, subsoil layering evaluated by cone penetration tests and&lt;br&gt;
groundwater level distribution for the relevant case study of San Carlo (Emilia Romagna-&lt;br&gt;
Italy) struck by a severe earthquake in 2012. Here liquefaction phenomena were observed on a&lt;br&gt;
portion of the village in the form of sand ejecta, lateral spreading and various damages on&lt;br&gt;
buildings and infrastructures. The location of damage allows to test possible relations with the&lt;br&gt;
factors characterizing susceptibility, triggering and severity of liquefaction. The relation&lt;br&gt;
among the different variables has been herein sought by training a specifically implemented&lt;br&gt;
Artificial Neural Network. A relation has thus been inferred between damage and thickness of&lt;br&gt;
the liquefiable layer and of the upper crust, seismic input and soil characteristics.&lt;/p&gt;</description>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/700748/">700748</awardNumber>
      <awardTitle>Assessment and mitigation of liquefaction potential across Europe: a holistic approach to protect structures / infrastructures for improved resilience to earthquake-induced liquefaction disasters</awardTitle>
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