publication . Other literature type . Conference object . Article . 2019 . Embargo end date: 30 Jun 2020

Prediction of liquefaction damage with artificial neural networks

Paolella Luca; Erminio, Salvatore; Spacagna Rose Line; Modoni Giuseppe; Ochmanski Maciej;
Open Access English
  • Published: 23 Jun 2019
  • Publisher: Zenodo
Abstract
The survey of the damage occurred on land, buildings and infrastructures<br> extensively affected by liquefaction, coupled with a comprehensive investigation of the subsoil<br> properties enables to identify the factors that determine the spatial distribution of the phenomenon.<br> With this goal, a database was created in a Geographic Information platform merging<br> records of local seismicity, subsoil layering evaluated by cone penetration tests and<br> groundwater level distribution for the relevant case study of San Carlo (Emilia Romagna-<br> Italy) struck by a severe earthquake in 2012. Here liquefaction phenomena were observed on a<br> portion of the vill...
Subjects
free text keywords: Liquefaction, Artificial Neural Networks
Funded by
EC| LIQUEFACT
Project
LIQUEFACT
Assessment and mitigation of liquefaction potential across Europe: a holistic approach to protect structures / infrastructures for improved resilience to earthquake-induced liquefaction disasters
  • Funder: European Commission (EC)
  • Project Code: 700748
  • Funding stream: H2020 | RIA
Validated by funder
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ZENODO
Conference object . 2019
Provider: ZENODO
Zenodo
Other literature type . 2019
Provider: Datacite
Zenodo
Other literature type . 2019
Provider: Datacite
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