Reprint
Application of Artificial Intelligence in Hydraulic Engineering
Edited by
July 2024
308 pages
- ISBN978-3-7258-1380-3 (Hardback)
- ISBN978-3-7258-1379-7 (PDF)
This book is a reprint of the Special Issue Application of Artificial Intelligence in Hydraulic Engineering that was published in
Biology & Life Sciences
Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Public Health & Healthcare
Summary
To ensure the safety of water and hydraulic engineering, we combined traditional computing techniques such as geotechnical tests, non-destructive testing, and numerical simulation. Intelligent algorithms will help us further understand various laws and mechanisms in water conservation projects, which is of great significance to improving the safety of water conservation projects and the development level of human society. We hope that this excellent collection of papers and data will be seen and utilized by more people.
Format
- Hardback
License and Copyright
© 2024 by the authors; CC BY-NC-ND license
Keywords
cut-off wall; monitoring section; numerical simulation; dynamic construction stability; bend sensor; computational simulation; individual measurement; resistive sensor; volume measurement; water consumption; multiple measuring points; concrete face rockfill dam; XGBoost; dam settlement monitoring; factor importance analysis; karst depression reservoir basin seepage; distributed temperature sensing (DTS); model test; phosphorus slag medium; temperature distribution; application of neural network; hydraulic supply system; urban reservoirs; centrifugal pump; splitter blades; CFD simulation; artificial fish swarm algorithm (AFSA); permeability coefficient; inversion analysis; orthogonal experimental design; RF; HHO; compact rock; gas permeability; underground storage caverns; urban river ecological management project; complex network; risk identification; risk transfer; dam surface cracks; transfer learning; intelligent detection; small-scale datasets; RCC dams; safety state; comprehensive evaluation; interval number theory; uncertainty; reservoir forecasting and operation; dynamic process; multi-time scales; integrated platform; complex networks; ecological management engineering; risk; SRIES model; urban rivers; coarse-grained soil; hydraulic conductivity; computed tomography image segmentation; convolutional neural network; deep learning; floodgates; vibration signal; noise reduction; ASVD algorithm; ICEEMDAN; n/a