Dynamic Inversion Method for Concrete Gravity Dam on Soft Rock Foundation
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| 出版年: | Applied Sciences vol. 15, no. 9 (2025), p. 4750 |
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| 第一著者: | |
| その他の著者: | , , , , |
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MDPI AG
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| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231338 |2 nlm | ||
| 100 | 1 | |a Yin Guanglin |u College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China; guanglin-yin@sac-china.com (G.Y.); 241302020021@hhu.edu.cn (W.X.); ltchhu@163.com (T.L.) | |
| 245 | 1 | |a Dynamic Inversion Method for Concrete Gravity Dam on Soft Rock Foundation | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This study provides a novel approach for assessing the long-term safety of the concrete gravity dam on a soft rock foundation. The proposed dynamic inversion method, based on an improved particle swarm optimization algorithm, enables accurate identification of time-dependent parameter deterioration in dam foundations. The proposed method provides practical solutions for real-time dam health monitoring, stability assessment, and maintenance optimization, enabling more reliable safety evaluations and informed engineering decisions. This study presents a dynamic inversion method for the concrete gravity dam on a soft rock foundation, aiming to accurately characterize the time-dependent trend of the dam’s mechanical properties. Conventional static inversion methods often overlook temporal variations in material behavior, particularly the long-term weakening of soft rock foundations under environmental influences. To address this limitation, an improved particle swarm optimization (PSO) algorithm is developed for dynamic parameter inversion, combining real-time monitoring data with finite element modeling to evaluate the time-varying elastic modulus of the foundation. The results reveal an exponential decay in the foundation’s elastic modulus (from 4.67 GPa to approximately 3.83 GPa), while the dam body maintains a stable modulus of 20.74 GPa. Comparative analyses demonstrate that the dynamic inversion approach, which accounts for time-dependent parameter degradation, significantly improves the displacement prediction accuracy of the dam. The results highlight the critical importance of incorporating temporal mechanical property variations in inversion analyses to ensure reliable structural assessments and enhance long-term dam safety management. | |
| 653 | |a Mechanical properties | ||
| 653 | |a Behavior | ||
| 653 | |a Accuracy | ||
| 653 | |a Thermal cycling | ||
| 653 | |a Deep learning | ||
| 653 | |a Gravity | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Concrete | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Optimization | ||
| 653 | |a Methods | ||
| 653 | |a Deformation | ||
| 653 | |a Hydraulics | ||
| 653 | |a Hydroelectric power | ||
| 700 | 1 | |a Lin Chaoning |u College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China; guanglin-yin@sac-china.com (G.Y.); 241302020021@hhu.edu.cn (W.X.); ltchhu@163.com (T.L.) | |
| 700 | 1 | |a Sheng Taozhen |u Center for Big Data and Smart Water, Nanjing Hydraulic Research Institute, Nanjing 210029, China; hhustz@126.com | |
| 700 | 1 | |a Xue Wenbo |u College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China; guanglin-yin@sac-china.com (G.Y.); 241302020021@hhu.edu.cn (W.X.); ltchhu@163.com (T.L.) | |
| 700 | 1 | |a Li Tongchun |u College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210024, China; guanglin-yin@sac-china.com (G.Y.); 241302020021@hhu.edu.cn (W.X.); ltchhu@163.com (T.L.) | |
| 700 | 1 | |a Chen Siyu |u Dam Safety Management Department, Nanjing Hydraulic Research Institute, Nanjing 210029, China | |
| 773 | 0 | |t Applied Sciences |g vol. 15, no. 9 (2025), p. 4750 | |
| 786 | 0 | |d ProQuest |t Publicly Available Content Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3203187404/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
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| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3203187404/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |