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Prediction of liquefaction damage with artificial neural networks

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


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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Paolella Luca</dc:creator>
  <dc:creator>Salvatore Erminio</dc:creator>
  <dc:creator>Spacagna Rose Line</dc:creator>
  <dc:creator>Modoni Giuseppe</dc:creator>
  <dc:creator>Ochmanski Maciej</dc:creator>
  <dc:date>2019-06-23</dc:date>
  <dc:date>info:eu-repo/date/embargoEnd/2020-06-30</dc:date>
  <dc:description>The survey of the damage occurred on land, buildings and infrastructures
extensively affected by liquefaction, coupled with a comprehensive investigation of the subsoil
properties enables to identify the factors that determine the spatial distribution of the phenomenon.
With this goal, a database was created in a Geographic Information platform merging
records of local seismicity, subsoil layering evaluated by cone penetration tests and
groundwater level distribution for the relevant case study of San Carlo (Emilia Romagna-
Italy) struck by a severe earthquake in 2012. Here liquefaction phenomena were observed on a
portion of the village in the form of sand ejecta, lateral spreading and various damages on
buildings and infrastructures. The location of damage allows to test possible relations with the
factors characterizing susceptibility, triggering and severity of liquefaction. The relation
among the different variables has been herein sought by training a specifically implemented
Artificial Neural Network. A relation has thus been inferred between damage and thickness of
the liquefiable layer and of the upper crust, seismic input and soil characteristics.</dc:description>
  <dc:identifier>https://zenodo.org/record/3463412</dc:identifier>
  <dc:identifier>10.5281/zenodo.3463412</dc:identifier>
  <dc:identifier>oai:zenodo.org:3463412</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/700748/</dc:relation>
  <dc:relation>doi:10.5281/zenodo.3463411</dc:relation>
  <dc:rights>info:eu-repo/semantics/embargoedAccess</dc:rights>
  <dc:rights>http://creativecommons.org/licenses/by/1.0/legalcode</dc:rights>
  <dc:subject>Liquefaction</dc:subject>
  <dc:subject>Artificial Neural Networks</dc:subject>
  <dc:title>Prediction of liquefaction damage with artificial neural networks</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
</oai_dc:dc>
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