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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/adce/6678669 |
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| author | Ayele Tesema Chala Mais Mayassah Clara Beatrice Vilceanu Richard Ray |
| author_facet | Ayele Tesema Chala Mais Mayassah Clara Beatrice Vilceanu Richard Ray |
| author_sort | Ayele Tesema Chala |
| collection | DOAJ |
| description | 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 linear model (GLM) to probabilistically predict peak ground acceleration (PGA) while accounting for uncertainties associated with geotechnical variability. Latin hypercube sampling (LHS) was employed to generate synthetic datasets of key geotechnical parameters, including plasticity index, shear wave velocity, soil thickness, input motion intensity, and unit weight of soil for hypothetical sites. Subsequently, a series of one-dimensional equivalent linear (1D-EQL) seismic site response analyses were performed, and PGA value at ground surface level were recorded for each analysis. The Bayesian GLM was then developed using these comprehensive datasets to probabilistically predict PGA. The performance and reliability of the developed model were evaluated on a separate test dataset. To benchmark its performance, a Bayesian neural network (BNN) was also developed and compared. In addition, a Shiny-based graphical user interface (GUI), named Bayes-PGA-predictor, was implemented to facilitate practical application. The findings demonstrate that the Bayesian GLM offers a robust and interpretable approach to predicting PGA while effectively quantifying uncertainty associated with geotechnical variability. |
| format | Article |
| id | doaj-art-250781077c4b45d9b2b51ad88e3d95fb |
| institution | Matheson Library |
| issn | 1687-8094 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-250781077c4b45d9b2b51ad88e3d95fb2025-07-02T00:00:17ZengWileyAdvances in Civil Engineering1687-80942025-01-01202510.1155/adce/6678669Quantifying Geotechnical Uncertainty in Ground Motion Predictions: Bayesian Generalized Linear Model FrameworkAyele Tesema Chala0Mais Mayassah1Clara Beatrice Vilceanu2Richard Ray3Structural and Geotechnical Engineering DepartmentStructural and Geotechnical Engineering DepartmentDepartment of Overland Communication WaysStructural and Geotechnical Engineering DepartmentAccurate 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 linear model (GLM) to probabilistically predict peak ground acceleration (PGA) while accounting for uncertainties associated with geotechnical variability. Latin hypercube sampling (LHS) was employed to generate synthetic datasets of key geotechnical parameters, including plasticity index, shear wave velocity, soil thickness, input motion intensity, and unit weight of soil for hypothetical sites. Subsequently, a series of one-dimensional equivalent linear (1D-EQL) seismic site response analyses were performed, and PGA value at ground surface level were recorded for each analysis. The Bayesian GLM was then developed using these comprehensive datasets to probabilistically predict PGA. The performance and reliability of the developed model were evaluated on a separate test dataset. To benchmark its performance, a Bayesian neural network (BNN) was also developed and compared. In addition, a Shiny-based graphical user interface (GUI), named Bayes-PGA-predictor, was implemented to facilitate practical application. The findings demonstrate that the Bayesian GLM offers a robust and interpretable approach to predicting PGA while effectively quantifying uncertainty associated with geotechnical variability.http://dx.doi.org/10.1155/adce/6678669 |
| spellingShingle | Ayele Tesema Chala Mais Mayassah Clara Beatrice Vilceanu Richard Ray Quantifying Geotechnical Uncertainty in Ground Motion Predictions: Bayesian Generalized Linear Model Framework Advances in Civil Engineering |
| title | Quantifying Geotechnical Uncertainty in Ground Motion Predictions: Bayesian Generalized Linear Model Framework |
| title_full | Quantifying Geotechnical Uncertainty in Ground Motion Predictions: Bayesian Generalized Linear Model Framework |
| title_fullStr | Quantifying Geotechnical Uncertainty in Ground Motion Predictions: Bayesian Generalized Linear Model Framework |
| title_full_unstemmed | Quantifying Geotechnical Uncertainty in Ground Motion Predictions: Bayesian Generalized Linear Model Framework |
| title_short | Quantifying Geotechnical Uncertainty in Ground Motion Predictions: Bayesian Generalized Linear Model Framework |
| title_sort | quantifying geotechnical uncertainty in ground motion predictions bayesian generalized linear model framework |
| url | http://dx.doi.org/10.1155/adce/6678669 |
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