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

Department of Geotechnics and Roads (RB/RB7)

University of Cassino and Southern Lazio

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

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.

Original language	English
Title of host publication	Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019
Editors	Francesco Silvestri, Nicola Moraci
Publisher	CRC Press/Balkema
Pages	4309-4316
Number of pages	8
ISBN (Print)	9780367143282
Publication status	Published - 2019
Event	7th International Conference on Earthquake Geotechnical Engineering, ICEGE 2019 - Rome, Italy Duration: 17 Jan 2019 → 20 Jan 2019

Publication series

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

Conference

Conference	7th International Conference on Earthquake Geotechnical Engineering, ICEGE 2019
Country/Territory	Italy
City	Rome
Period	17/01/19 → 20/01/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

ASJC Scopus subject areas

General Environmental Science
General Earth and Planetary Sciences

Cite this

Paolella, L., Salvatore, E., Spacagna, R. L., Modoni, G., & Ochmański, M. (2019). Prediction of liquefaction damage with artificial neural networks. In F. Silvestri, & N. Moraci (Eds.), Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions- Proceedings of the 7th International Conference on Earthquake Geotechnical Engineering, 2019 (pp. 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. et al. / 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. editor / Francesco Silvestri ; Nicola Moraci. CRC Press/Balkema, 2019. pp. 4309-4316 (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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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",

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language = "English",

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Paolella, L, Salvatore, E, Spacagna, RL, Modoni, G & Ochmański, M 2019, Prediction of liquefaction damage with artificial neural networks. in F Silvestri & N Moraci (eds), 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, pp. 4309-4316, 7th International Conference on Earthquake Geotechnical Engineering, ICEGE 2019, Rome, Italy, 17/01/19.

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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. In Silvestri F, Moraci N, editors, 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. p. 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

Abstract

Publication series

Conference

UN SDGs

ASJC Scopus subject areas

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