Quantifying Geotechnical Uncertainty in Ground Motion Predictions: Bayesian Generalized Linear Model Framework
Accurate prediction of peak ground intensity measures is inevitably influenced by geotechnical variability. Variations in soil properties, subsurface conditions, and seismic inputs introduce complexities that challenge the reliability of predictions. This study introduces a Bayesian generalized line...
Saved in:
Main Authors: | Ayele Tesema Chala, Mais Mayassah, Clara Beatrice Vilceanu, Richard Ray |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2025-01-01
|
Series: | Advances in Civil Engineering |
Online Access: | http://dx.doi.org/10.1155/adce/6678669 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Bayesian analysis of linear models
by: Broemeling, Lyle D., 1939-
Published: (1985) -
Bayesian distances for quantifying tensions in cosmological inference and the surprise statistic
by: Benedikt Schosser, et al.
Published: (2025-02-01) -
THE IMPORTANCE OF A COMBINED 3D MODEL OF GROUND AND GEOTECHNICAL STRUCTURE
by: Ivan Vaníček, et al.
Published: (2022-01-01) -
Strong ground motion seismology /
Published: (1987) -
Quantifying uncertainty in time perception: A modified reproduction method
by: Jaume Boned, et al.
Published: (2024-11-01)