The seismic and liquefaction risk assessment implies introducing methods based on different hypotheses and dealing with different levels of uncertainty affecting the whole process from triggering to surfi cial manifestation. In this context, soft computing methods, like Bayesian Belief Networks (BBN) and artificial intelligence algorithms, provide the logic framework for cause-effect relationships and the statistical statement to manage uncertainties. Taking advantage of the significant amount of geotechnical data and post-earthquake surveys, an application of BBN versus the forecasting of liquefaction-induced ground damage is proposed con sidering three main shocks of the 2010 – 2011 Christchurch (New Zealand) Earthquakes Sequence. The BBN algorithms are firstly employed to identify significant variables and learn the relationships among them, then a direct and graphical link between input and target data is created. The quantitative validation of the built architecture enables to advantageously queried the net to predict the result of new datasets.

Liquefaction damage assessment using Bayesian belief networks

Paolella, L.
Writing – Original Draft Preparation
;
Baris, A.
Validation
;
Modoni, G.
Conceptualization
;
Spacagna, R. L.
Supervision
;
2022

Abstract

The seismic and liquefaction risk assessment implies introducing methods based on different hypotheses and dealing with different levels of uncertainty affecting the whole process from triggering to surfi cial manifestation. In this context, soft computing methods, like Bayesian Belief Networks (BBN) and artificial intelligence algorithms, provide the logic framework for cause-effect relationships and the statistical statement to manage uncertainties. Taking advantage of the significant amount of geotechnical data and post-earthquake surveys, an application of BBN versus the forecasting of liquefaction-induced ground damage is proposed con sidering three main shocks of the 2010 – 2011 Christchurch (New Zealand) Earthquakes Sequence. The BBN algorithms are firstly employed to identify significant variables and learn the relationships among them, then a direct and graphical link between input and target data is created. The quantitative validation of the built architecture enables to advantageously queried the net to predict the result of new datasets.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11580/91740
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