3463412
doi
10.5281/zenodo.3463412
oai:zenodo.org:3463412
user-eu
Salvatore Erminio
University of Cassino and Southern Lazio
Spacagna Rose Line
University of Cassino and Southern Lazio
Modoni Giuseppe
University of Cassino and Southern Lazio
Ochmanski Maciej
Silesian University of Technology - Gliwice (Poland)
Prediction of liquefaction damage with artificial neural networks
Paolella Luca
University of Cassino and Southern Lazio
info:eu-repo/semantics/openAccess
Creative Commons Attribution 1.0 Generic
https://creativecommons.org/licenses/by/1.0/legalcode
Liquefaction
Artificial Neural Networks
<p>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 village in the form of sand ejecta, lateral spreading and various damages on<br>
buildings and infrastructures. The location of damage allows to test possible relations with the<br>
factors characterizing susceptibility, triggering and severity of liquefaction. The relation<br>
among the different variables has been herein sought by training a specifically implemented<br>
Artificial Neural Network. A relation has thus been inferred between damage and thickness of<br>
the liquefiable layer and of the upper crust, seismic input and soil characteristics.</p>
Zenodo
2019-06-23
info:eu-repo/semantics/conferencePaper
3463411
user-eu
award_title=Assessment and mitigation of liquefaction potential across Europe: a holistic approach to protect structures / infrastructures for improved resilience to earthquake-induced liquefaction disasters; award_number=700748; award_identifiers_scheme=url; award_identifiers_identifier=https://cordis.europa.eu/projects/700748; funder_id=00k4n6c32; funder_name=European Commission;
1593478794.264675
688906
md5:03e6843edfe65fd1fbb2c254c37ad21f
https://zenodo.org/records/3463412/files/ch476.pdf
public
10.5281/zenodo.3463411
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doi