Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring
With the gradual transformation of hydraulic engineering from digitization and intelligence to WISDOM, remote sensing technology, artificial intelligence and deep learning methods have been widely used for automatic perception, processing, storage and analysis of hydraulic structure engineering moni...
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| Format: | Online |
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| Jezik: | engleski |
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MDPI - Multidisciplinary Digital Publishing Institute
2026
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| Online pristup: | ONIX_20260416T142754_9783725854455_18 |
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| collection | Directory of Open Access Books |
| description | With the gradual transformation of hydraulic engineering from digitization and intelligence to WISDOM, remote sensing technology, artificial intelligence and deep learning methods have been widely used for automatic perception, processing, storage and analysis of hydraulic structure engineering monitoring data. The advent of remote sensing technologies such as three-dimensional tilt photography offers the opportunity to build an integrated hydraulic engineering monitoring and acquisition system that is capable of capturing all the details of hydraulic engineering. With the introduction of artificial intelligence and deep learning methods, hydraulic engineering information has been analyzed and exploited efficiently. Combined with traditional hydraulic structure behavior analysis methods, such as geotechnical testing and numerical simulation, artificial intelligence and deep learning methods can help solve more complex hydraulic engineering problems by providing more accurate and professional intelligent analysis and ubiquitous hydraulic engineering services of great theoretical importance and application value in order to achieve the general improvement of the safety monitoring of hydraulic structures. |
| format | Online |
| id | doab-20.500.12854ir-174763 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1747632026-04-16T16:17:25Z Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring Shao, Chenfei Gu, Hao Xu, Yanxin Chen, Huixiang Qin, Xiangnan Yang, Guang Cofferdam Double-row sheet pile Soft-ground foundation Structural stability Finite element method Residual neural network Knowledge distillation Transfer learning Concrete dam Crack detection Deformation prediction model Sluice Long short-term memory Seagull optimization algorithm Weighted Markov model UAV Ice jam Frazil ice floe Remote sensing Frazil ice jam Teledetection Ice morphometry Data processing High concrete dam In situ observation data Temperature field Temperature control index Information entropy Cloud model Dam safety Sluice seepage Prediction model MHHO BiLSTM Dam monitoring data Gross errors Environmental change FCM algorithm OPTICS algorithm LOF algorithm Streamflow forecast Machine learning Statistical techniques Göksu Stream Nonuniform deformation Random coefficient model Individual effect values Maximum entropy principle Swin Transformer Vision Transformer Feature fusion Hydraulic structure Soft foundation Integrated numerical analysis Time-dependent behavior Global sensitivity analysis BP neural network Pumped storage power station Core wall rockfill dam Phreatic line Vertical earth pressure Deformation Settlement prediction model Concrete face rockfill dam Variational mode decomposition Harris hawks optimization Support vector regression Factor mining Coal mine Underground reservoir Coal pillar dam Artificial dam Stability Structure safety monitoring Deformation prediction Artificial intelligence Feature engineering Stacking method Performance indicators N A thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology With the gradual transformation of hydraulic engineering from digitization and intelligence to WISDOM, remote sensing technology, artificial intelligence and deep learning methods have been widely used for automatic perception, processing, storage and analysis of hydraulic structure engineering monitoring data. The advent of remote sensing technologies such as three-dimensional tilt photography offers the opportunity to build an integrated hydraulic engineering monitoring and acquisition system that is capable of capturing all the details of hydraulic engineering. With the introduction of artificial intelligence and deep learning methods, hydraulic engineering information has been analyzed and exploited efficiently. Combined with traditional hydraulic structure behavior analysis methods, such as geotechnical testing and numerical simulation, artificial intelligence and deep learning methods can help solve more complex hydraulic engineering problems by providing more accurate and professional intelligent analysis and ubiquitous hydraulic engineering services of great theoretical importance and application value in order to achieve the general improvement of the safety monitoring of hydraulic structures. 2026-04-16T16:17:17Z 2026-04-16T16:17:17Z 2025 book ONIX_20260416T142754_9783725854455_18 9783725854455 9783725854462 https://directory.doabooks.org/handle/20.500.12854/174763 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/ https://mdpi.com/books/pdfview/book/11641 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-5446-2 10.3390/books978-3-7258-5446-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725854455 9783725854462 306 CH open access |
| spellingShingle | Cofferdam Double-row sheet pile Soft-ground foundation Structural stability Finite element method Residual neural network Knowledge distillation Transfer learning Concrete dam Crack detection Deformation prediction model Sluice Long short-term memory Seagull optimization algorithm Weighted Markov model UAV Ice jam Frazil ice floe Remote sensing Frazil ice jam Teledetection Ice morphometry Data processing High concrete dam In situ observation data Temperature field Temperature control index Information entropy Cloud model Dam safety Sluice seepage Prediction model MHHO BiLSTM Dam monitoring data Gross errors Environmental change FCM algorithm OPTICS algorithm LOF algorithm Streamflow forecast Machine learning Statistical techniques Göksu Stream Nonuniform deformation Random coefficient model Individual effect values Maximum entropy principle Swin Transformer Vision Transformer Feature fusion Hydraulic structure Soft foundation Integrated numerical analysis Time-dependent behavior Global sensitivity analysis BP neural network Pumped storage power station Core wall rockfill dam Phreatic line Vertical earth pressure Deformation Settlement prediction model Concrete face rockfill dam Variational mode decomposition Harris hawks optimization Support vector regression Factor mining Coal mine Underground reservoir Coal pillar dam Artificial dam Stability Structure safety monitoring Deformation prediction Artificial intelligence Feature engineering Stacking method Performance indicators N A thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring |
| title | Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring |
| title_full | Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring |
| title_fullStr | Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring |
| title_full_unstemmed | Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring |
| title_short | Remote Sensing, Artificial Intelligence and Deep Learning in Hydraulic Structure Safety Monitoring |
| title_sort | remote sensing artificial intelligence and deep learning in hydraulic structure safety monitoring |
| topic | Cofferdam Double-row sheet pile Soft-ground foundation Structural stability Finite element method Residual neural network Knowledge distillation Transfer learning Concrete dam Crack detection Deformation prediction model Sluice Long short-term memory Seagull optimization algorithm Weighted Markov model UAV Ice jam Frazil ice floe Remote sensing Frazil ice jam Teledetection Ice morphometry Data processing High concrete dam In situ observation data Temperature field Temperature control index Information entropy Cloud model Dam safety Sluice seepage Prediction model MHHO BiLSTM Dam monitoring data Gross errors Environmental change FCM algorithm OPTICS algorithm LOF algorithm Streamflow forecast Machine learning Statistical techniques Göksu Stream Nonuniform deformation Random coefficient model Individual effect values Maximum entropy principle Swin Transformer Vision Transformer Feature fusion Hydraulic structure Soft foundation Integrated numerical analysis Time-dependent behavior Global sensitivity analysis BP neural network Pumped storage power station Core wall rockfill dam Phreatic line Vertical earth pressure Deformation Settlement prediction model Concrete face rockfill dam Variational mode decomposition Harris hawks optimization Support vector regression Factor mining Coal mine Underground reservoir Coal pillar dam Artificial dam Stability Structure safety monitoring Deformation prediction Artificial intelligence Feature engineering Stacking method Performance indicators N A thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | Cofferdam Double-row sheet pile Soft-ground foundation Structural stability Finite element method Residual neural network Knowledge distillation Transfer learning Concrete dam Crack detection Deformation prediction model Sluice Long short-term memory Seagull optimization algorithm Weighted Markov model UAV Ice jam Frazil ice floe Remote sensing Frazil ice jam Teledetection Ice morphometry Data processing High concrete dam In situ observation data Temperature field Temperature control index Information entropy Cloud model Dam safety Sluice seepage Prediction model MHHO BiLSTM Dam monitoring data Gross errors Environmental change FCM algorithm OPTICS algorithm LOF algorithm Streamflow forecast Machine learning Statistical techniques Göksu Stream Nonuniform deformation Random coefficient model Individual effect values Maximum entropy principle Swin Transformer Vision Transformer Feature fusion Hydraulic structure Soft foundation Integrated numerical analysis Time-dependent behavior Global sensitivity analysis BP neural network Pumped storage power station Core wall rockfill dam Phreatic line Vertical earth pressure Deformation Settlement prediction model Concrete face rockfill dam Variational mode decomposition Harris hawks optimization Support vector regression Factor mining Coal mine Underground reservoir Coal pillar dam Artificial dam Stability Structure safety monitoring Deformation prediction Artificial intelligence Feature engineering Stacking method Performance indicators N A thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20260416T142754_9783725854455_18 |