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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    <subfield code="a">Liquefaction</subfield>
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    <subfield code="a">Artificial Neural Networks</subfield>
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    <subfield code="u">University of Cassino and Southern Lazio</subfield>
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    <subfield code="u">Silesian University of Technology - Gliwice (Poland)</subfield>
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    <subfield code="u">University of Cassino and Southern Lazio</subfield>
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    <subfield code="a">Prediction of liquefaction damage with artificial neural networks</subfield>
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    <subfield code="c">700748</subfield>
    <subfield code="a">Assessment and mitigation of liquefaction potential across Europe: a holistic approach to protect structures / infrastructures for improved resilience to earthquake-induced liquefaction disasters</subfield>
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    <subfield code="a">&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;</subfield>
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