A new Graph Neural Network (GNN) based model for the evaluation of lateral spreading displacement in New Zealand
Author:
Affiliation:
1. University of Calabria
2. Univeristy of Texas at Austin
Publisher
The Japanese Geotechnical Society
Link
https://www.jstage.jst.go.jp/article/jgssp/10/21/10_v10.OS-10-04/_pdf
Reference22 articles.
1. 1) Anselin L (1995), Local Indicators of Spatial Association—LISA. Geographical Analysis, 27: 93-115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
2. 2) Bird JF, Bommer JJ, Crowley H and Pinho R (2006). Modelling liquefaction-induced building damage in earthquake loss estimation. Soil Dynamics and Earthquake Engineering 26(1): 15–30.
3. 3) Breiman L (2001). Random Forests. Machine Learning, 45(1), 5–32.
4. 4) Cubrinovski M, Winkley A, Haskell J, Palermo A, Wotherspoon L, Robinson K, Bradley B, Brabhaharan P and Hughes M (2014) Spreading-induced damage to short-span bridges in Christchurch, New Zealand. Earthquake Spectra 30(1): 57–83.
5. 5) Durante MG, Rathje EM (2021a). An exploration of the use of machine learning to predict lateral spreading. Earthquake Spectra, Vol. 37(4) 2288–2314.
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