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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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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3463412", 
  "language": "eng", 
  "title": "Prediction of liquefaction damage with artificial neural networks", 
  "issued": {
    "date-parts": [
      [
        2019, 
        6, 
        23
      ]
    ]
  }, 
  "abstract": "<p>The survey of the damage occurred on land, buildings and infrastructures<br>\nextensively affected by liquefaction, coupled with a comprehensive investigation of the subsoil<br>\nproperties enables to identify the factors that determine the spatial distribution of the phenomenon.<br>\nWith this goal, a database was created in a Geographic Information platform merging<br>\nrecords of local seismicity, subsoil layering evaluated by cone penetration tests and<br>\ngroundwater level distribution for the relevant case study of San Carlo (Emilia Romagna-<br>\nItaly) struck by a severe earthquake in 2012. Here liquefaction phenomena were observed on a<br>\nportion of the village in the form of sand ejecta, lateral spreading and various damages on<br>\nbuildings and infrastructures. The location of damage allows to test possible relations with the<br>\nfactors characterizing susceptibility, triggering and severity of liquefaction. The relation<br>\namong the different variables has been herein sought by training a specifically implemented<br>\nArtificial Neural Network. A relation has thus been inferred between damage and thickness of<br>\nthe liquefiable layer and of the upper crust, seismic input and soil characteristics.</p>", 
  "author": [
    {
      "family": "Paolella Luca"
    }, 
    {
      "family": "Salvatore Erminio"
    }, 
    {
      "family": "Spacagna Rose Line"
    }, 
    {
      "family": "Modoni Giuseppe"
    }, 
    {
      "family": "Ochmanski Maciej"
    }
  ], 
  "type": "paper-conference", 
  "id": "3463412"
}
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