Prediction of liquefaction damage with artificial neural networks

L. Paolella; E. Salvatore; R. L. Spacagna; G. Modoni; M. Ochmański

Prediction of liquefaction damage with artificial neural networks

L. Paolella
, E. Salvatore
, R. L. Spacagna
, G. Modoni
, M. Ochmański

Katedra Geotechniki i Dróg (RB/RB7)

University of Cassino and Southern Lazio

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

5 Cytowania z bazy Scopus

Abstrakt

The survey of the damage occurred on land, buildings and infrastructures extensively affected by liquefaction, coupled with a comprehensive investigation of the subsoil properties enables to identify the factors that determine the spatial distribution of the phenomenon. With this goal, a database was created in a Geographic Information platform merging records of local seismicity, subsoil layering evaluated by cone penetration tests and groundwater level distribution for the relevant case study of San Carlo (Emilia Romagna-Italy) struck by a severe earthquake in 2012. Here liquefaction phenomena were observed on a portion of the village in the form of sand ejecta, lateral spreading and various damages on buildings and infrastructures. The location of damage allows to test possible relations with the factors characterizing susceptibility, triggering and severity of liquefaction. The relation among the different variables has been herein sought by training a specifically implemented Artificial Neural Network. A relation has thus been inferred between damage and thickness of the liquefiable layer and of the upper crust, seismic input and soil characteristics.

Język oryginału	angielski
Tytuł publikacji goszczącej	Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019
Redaktorzy	Francesco Silvestri, Nicola Moraci
Wydawca	CRC Press/Balkema
Strony	4309-4316
Liczba stron	8
ISBN (drukowany)	9780367143282
Status publikacji	Opublikowano - 2019
Wydarzenie	7th International Conference on Earthquake Geotechnical Engineering, ICEGE 2019 - Rome, Włochy Czas trwania: 17 sty 2019 → 20 sty 2019

Seria publikacji

Nazwa	Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019

Konferencja

Konferencja	7th International Conference on Earthquake Geotechnical Engineering, ICEGE 2019
Kraj/Terytorium	Włochy
Miejscowość	Rome
Okres	17/01/19 → 20/01/19

Cele SDG ONZ

Ten wynik przyczynia się do realizacji następujących celów zrównoważonego rozwoju

Cel 11 Zrównoważone miasta i społeczności

Obszary tematyczne ASJC Scopus

Ogólne nauki o środowisku
Ogólne nauki o Ziemi i planetach

Inne pliki i linki

Link do publikacji w Scopus

Cytowanie

Paolella, L., Salvatore, E., Spacagna, R. L., Modoni, G., & Ochmański, M. (2019). Prediction of liquefaction damage with artificial neural networks. W F. Silvestri, & N. Moraci (Red.), Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019 (str. 4309-4316). (Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019). CRC Press/Balkema.

Paolella, L. ; Salvatore, E. ; Spacagna, R. L. i in. / Prediction of liquefaction damage with artificial neural networks. Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019. redaktor / Francesco Silvestri ; Nicola Moraci. CRC Press/Balkema, 2019. str. 4309-4316 (Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019).

@inproceedings{713218c2b9f744ab93dccaa4de3a3766,

title = "Prediction of liquefaction damage with artificial neural networks",

abstract = "The survey of the damage occurred on land, buildings and infrastructures extensively affected by liquefaction, coupled with a comprehensive investigation of the subsoil properties enables to identify the factors that determine the spatial distribution of the phenomenon. With this goal, a database was created in a Geographic Information platform merging records of local seismicity, subsoil layering evaluated by cone penetration tests and groundwater level distribution for the relevant case study of San Carlo (Emilia Romagna-Italy) struck by a severe earthquake in 2012. Here liquefaction phenomena were observed on a portion of the village in the form of sand ejecta, lateral spreading and various damages on buildings and infrastructures. The location of damage allows to test possible relations with the factors characterizing susceptibility, triggering and severity of liquefaction. The relation among the different variables has been herein sought by training a specifically implemented Artificial Neural Network. A relation has thus been inferred between damage and thickness of the liquefiable layer and of the upper crust, seismic input and soil characteristics.",

author = "L. Paolella and E. Salvatore and Spacagna, \{R. L.\} and G. Modoni and M. Ochma{\'n}ski",

note = "Publisher Copyright: {\textcopyright} 2019 Associazione Geotecnica Italiana, Rome, Italy.; 7th International Conference on Earthquake Geotechnical Engineering, ICEGE 2019 ; Conference date: 17-01-2019 Through 20-01-2019",

year = "2019",

language = "English",

isbn = "9780367143282",

series = "Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019",

publisher = "CRC Press/Balkema",

pages = "4309--4316",

editor = "Francesco Silvestri and Nicola Moraci",

booktitle = "Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019",

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Paolella, L, Salvatore, E, Spacagna, RL, Modoni, G & Ochmański, M 2019, Prediction of liquefaction damage with artificial neural networks. w F Silvestri & N Moraci (red), Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019. Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019, CRC Press/Balkema, str. 4309-4316, 7th International Conference on Earthquake Geotechnical Engineering, ICEGE 2019, Rome, Włochy, 17/01/19.

Prediction of liquefaction damage with artificial neural networks. / Paolella, L.; Salvatore, E.; Spacagna, R. L. i in.
Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019. red. / Francesco Silvestri; Nicola Moraci. CRC Press/Balkema, 2019. str. 4309-4316 (Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019).

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

TY - GEN

T1 - Prediction of liquefaction damage with artificial neural networks

AU - Paolella, L.

AU - Salvatore, E.

AU - Spacagna, R. L.

AU - Modoni, G.

AU - Ochmański, M.

PY - 2019

Y1 - 2019

N2 - The survey of the damage occurred on land, buildings and infrastructures extensively affected by liquefaction, coupled with a comprehensive investigation of the subsoil properties enables to identify the factors that determine the spatial distribution of the phenomenon. With this goal, a database was created in a Geographic Information platform merging records of local seismicity, subsoil layering evaluated by cone penetration tests and groundwater level distribution for the relevant case study of San Carlo (Emilia Romagna-Italy) struck by a severe earthquake in 2012. Here liquefaction phenomena were observed on a portion of the village in the form of sand ejecta, lateral spreading and various damages on buildings and infrastructures. The location of damage allows to test possible relations with the factors characterizing susceptibility, triggering and severity of liquefaction. The relation among the different variables has been herein sought by training a specifically implemented Artificial Neural Network. A relation has thus been inferred between damage and thickness of the liquefiable layer and of the upper crust, seismic input and soil characteristics.

AB - The survey of the damage occurred on land, buildings and infrastructures extensively affected by liquefaction, coupled with a comprehensive investigation of the subsoil properties enables to identify the factors that determine the spatial distribution of the phenomenon. With this goal, a database was created in a Geographic Information platform merging records of local seismicity, subsoil layering evaluated by cone penetration tests and groundwater level distribution for the relevant case study of San Carlo (Emilia Romagna-Italy) struck by a severe earthquake in 2012. Here liquefaction phenomena were observed on a portion of the village in the form of sand ejecta, lateral spreading and various damages on buildings and infrastructures. The location of damage allows to test possible relations with the factors characterizing susceptibility, triggering and severity of liquefaction. The relation among the different variables has been herein sought by training a specifically implemented Artificial Neural Network. A relation has thus been inferred between damage and thickness of the liquefiable layer and of the upper crust, seismic input and soil characteristics.

UR - https://www.scopus.com/pages/publications/85081174336

M3 - Conference contribution

AN - SCOPUS:85081174336

SN - 9780367143282

T3 - Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019

SP - 4309

EP - 4316

BT - Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019

A2 - Silvestri, Francesco

A2 - Moraci, Nicola

PB - CRC Press/Balkema

T2 - 7th International Conference on Earthquake Geotechnical Engineering, ICEGE 2019

Y2 - 17 January 2019 through 20 January 2019

ER -

Paolella L, Salvatore E, Spacagna RL, Modoni G, Ochmański M. Prediction of liquefaction damage with artificial neural networks. W Silvestri F, Moraci N, redaktorzy, Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019. CRC Press/Balkema. 2019. str. 4309-4316. (Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019).

Prediction of liquefaction damage with artificial neural networks

Abstrakt

Seria publikacji

Konferencja

Cele SDG ONZ

Obszary tematyczne ASJC Scopus

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