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Artificial neural network-aided decoupled prediction of earthquake-induced shallow and deep sliding displacements of slopes.
- Source :
-
Computers & Geotechnics . Dec2023, Vol. 164, pN.PAG-N.PAG. 1p. - Publication Year :
- 2023
-
Abstract
- The Newmark-type predictive models are extensively used to estimate earthquake-induced sliding displacements (D) of slopes. Most existing models are designed for shallow slope failure using polynomial regression. Based on a large amount of decoupled sliding-block analyses, this paper proposes an artificial neural network (ANN)-aided model to predict D for both shallow and deep slope failures using peak ground acceleration and spectral acceleration at the 2 s period (SA (2 s)). Three sub-models are included for estimating shallow sliding displacement, representing dynamic response of sliding mass, and modifying the displacement for deep failure, respectively. The key features achieved are as follows: (1) the inputted SA is more easily accessible than the mean period (T m) required by the existing models; (2) impedance ratio (IR) is utilized to account for stiffness conditions of geo-materials underlying slip surfaces; (3) powerful ANN is introduced for the first time as a surrogate of the decoupled analysis. The SA -based model generally yields lower biases and uncertainty than the existing models. Since larger D is produced for smaller IR , the existing models with a single IR value would be unconservative for relatively stiff underlying soils and/or soft overlying soils. Coefficients of the proposed model are provided for practical applications. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PREDICTION models
*ARTIFICIAL neural networks
*SLOPE stability
*LANDSLIDES
Subjects
Details
- Language :
- English
- ISSN :
- 0266352X
- Volume :
- 164
- Database :
- Academic Search Index
- Journal :
- Computers & Geotechnics
- Publication Type :
- Academic Journal
- Accession number :
- 173120584
- Full Text :
- https://doi.org/10.1016/j.compgeo.2023.105844