Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam
Computational modeling plays a significant role in the design of rockfill dams. Various constitutive soil parameters are used to design such models, which often involve high uncertainties due to the complex structure of rockfill dams comprising various zones of different soil parameters. This study...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/13/13/1830 |
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| author | Gullnaz Shahzadi Azzeddine Soulaïmani |
| author_facet | Gullnaz Shahzadi Azzeddine Soulaïmani |
| author_sort | Gullnaz Shahzadi |
| collection | DOAJ |
| description | Computational modeling plays a significant role in the design of rockfill dams. Various constitutive soil parameters are used to design such models, which often involve high uncertainties due to the complex structure of rockfill dams comprising various zones of different soil parameters. This study performs an uncertainty analysis and a global sensitivity analysis to assess the effect of constitutive soil parameters on the behavior of a rockfill dam. A Finite Element code (Plaxis) is utilized for the structure analysis. A database of the computed displacements at inclinometers installed in the dam is generated and compared to in situ measurements. Surrogate models are significant tools for approximating the relationship between input soil parameters and displacements and thereby reducing the computational costs of parametric studies. Polynomial chaos expansion and deep neural networks are used to build surrogate models to compute the Sobol indices required to identify the impact of soil parameters on dam behavior. |
| first_indexed | 2024-03-10T09:53:59Z |
| format | Article |
| id | doaj.art-a511af73167043729a4369ecc5a9c045 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T09:53:59Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-a511af73167043729a4369ecc5a9c0452023-11-22T02:32:40ZengMDPI AGWater2073-44412021-06-011313183010.3390/w13131830Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill DamGullnaz Shahzadi0Azzeddine Soulaïmani1Department of Mechanical Engineering, École de Technologie Supérieure, 1100 Notre-Dame W., Montréal, QC H3C 1K3, CanadaDepartment of Mechanical Engineering, École de Technologie Supérieure, 1100 Notre-Dame W., Montréal, QC H3C 1K3, CanadaComputational modeling plays a significant role in the design of rockfill dams. Various constitutive soil parameters are used to design such models, which often involve high uncertainties due to the complex structure of rockfill dams comprising various zones of different soil parameters. This study performs an uncertainty analysis and a global sensitivity analysis to assess the effect of constitutive soil parameters on the behavior of a rockfill dam. A Finite Element code (Plaxis) is utilized for the structure analysis. A database of the computed displacements at inclinometers installed in the dam is generated and compared to in situ measurements. Surrogate models are significant tools for approximating the relationship between input soil parameters and displacements and thereby reducing the computational costs of parametric studies. Polynomial chaos expansion and deep neural networks are used to build surrogate models to compute the Sobol indices required to identify the impact of soil parameters on dam behavior.https://www.mdpi.com/2073-4441/13/13/1830sensitivity analysispolynomial chaos expansionuncertaintydeep neural networksrockfill dams |
| spellingShingle | Gullnaz Shahzadi Azzeddine Soulaïmani Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam Water sensitivity analysis polynomial chaos expansion uncertainty deep neural networks rockfill dams |
| title | Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam |
| title_full | Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam |
| title_fullStr | Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam |
| title_full_unstemmed | Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam |
| title_short | Deep Neural Network and Polynomial Chaos Expansion-Based Surrogate Models for Sensitivity and Uncertainty Propagation: An Application to a Rockfill Dam |
| title_sort | deep neural network and polynomial chaos expansion based surrogate models for sensitivity and uncertainty propagation an application to a rockfill dam |
| topic | sensitivity analysis polynomial chaos expansion uncertainty deep neural networks rockfill dams |
| url | https://www.mdpi.com/2073-4441/13/13/1830 |
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