Conference paper Embargoed Access

Prediction of liquefaction damage with artificial neural networks

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


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        <foaf:name>Paolella Luca</foaf:name>
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        <foaf:name>Ochmanski Maciej</foaf:name>
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            <foaf:name>Silesian University of Technology - Gliwice (Poland)</foaf:name>
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    <dct:title>Prediction of liquefaction damage with artificial neural networks</dct:title>
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    <dcat:keyword>Liquefaction</dcat:keyword>
    <dcat:keyword>Artificial Neural Networks</dcat:keyword>
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        <foaf:name>European Commission</foaf:name>
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    <dct:description>&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;</dct:description>
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