An Explainable Probabilistic Model for Health Monitoring of Concrete Dam via Optimized Sparse Bayesian Learning and Sensitivity Analysis
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| Publicado en: | Structural Control and Health Monitoring (Online) vol. 2023, no. 1 (2023) |
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| Otros Autores: | , , |
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John Wiley & Sons, Inc.
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| Acceso en línea: | Citation/Abstract |
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|---|---|---|---|
| 001 | 3164359222 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1545-2263 | ||
| 024 | 7 | |a 10.1155/2023/2979822 |2 doi | |
| 035 | |a 3164359222 | ||
| 045 | 2 | |b d20230101 |b d20231231 | |
| 084 | |a 239329 |2 nlm | ||
| 100 | 1 | |a Lin Chaoning |u College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, Jiangsu, China, hhu.edu.cn; College of Civil and Transportation Engineering, Hohai University, Nanjing, Jiangsu, China, hhu.edu.cn | |
| 245 | 1 | |a An Explainable Probabilistic Model for Health Monitoring of Concrete Dam via Optimized Sparse Bayesian Learning and Sensitivity Analysis | |
| 260 | |b John Wiley & Sons, Inc. |c 2023 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Machine learning has become increasingly popular for modeling dam behavior due to its ability to capture complex relationships between input parameters and dam behavior responses. However, the use of sophisticated machine learning methods for monitoring dam behaviors and making decisions is often hindered by model uncertainty and a lack of interpretability. This paper introduces a novel model for dam health monitoring, focused on monitoring radial displacement and seepage, using optimized sparse Bayesian learning and sensitivity analysis. The model hyperparameters are optimized using an intelligent optimization method integrating the multi‐population Rao algorithm and blocked cross‐validation, while sensitivity analysis is employed to calculate the relative importance of input variables for a better understanding of the dam’s state. The effectiveness of the proposed model is verified by using long‐term monitoring data of a prototype concrete arch dam. The results confirm that the proposed model provides satisfactory performance on both the point predictions and the interval predictions for dam structural behaviors while obtaining effective explainability. | |
| 653 | |a Concrete dams | ||
| 653 | |a Structural behavior | ||
| 653 | |a Arch dams | ||
| 653 | |a Machine learning | ||
| 653 | |a Behavior | ||
| 653 | |a Probabilistic models | ||
| 653 | |a Monitoring methods | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Sensitivity analysis | ||
| 653 | |a Parameter sensitivity | ||
| 653 | |a Seepage | ||
| 653 | |a Effectiveness | ||
| 653 | |a Algorithms | ||
| 653 | |a Parameter uncertainty | ||
| 653 | |a Detention dams | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Chen Siyu |u Dam Safety Management Department, Nanjing Hydraulic Research Institute, Nanjing, Jiangsu, China, nhri.cn | |
| 700 | 1 | |a Hariri-Ardebili Mohammad Amin |u Department of Civil Environmental and Architectural Engineering, University of Colorado, Boulder, CO, USA, colorado.edu; College of Computer, Mathematical and Natural Sciences, University of Maryland, College Park, MD, USA, umaryland.edu | |
| 700 | 1 | |a Li Tongchun |u College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, Jiangsu, China, hhu.edu.cn | |
| 773 | 0 | |t Structural Control and Health Monitoring (Online) |g vol. 2023, no. 1 (2023) | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3164359222/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |