Soft Computing and Machine Learning in Dam Engineering

Edited by
May 2023
260 pages
  • ISBN978-3-0365-7579-7 (Hardback)
  • ISBN978-3-0365-7578-0 (PDF)

This book is a reprint of the Special Issue Soft Computing and Machine Learning in Dam Engineering that was published in

Biology & Life Sciences
Chemistry & Materials Science
Environmental & Earth Sciences
Public Health & Healthcare

“Soft Computing and Machine Learning in Dam Engineering” is a comprehensive, edited Special Issue that explores the latest advances in the application of soft computing and machine learning techniques to dam engineering. This reprint covers a range of topics, including dam design, construction, monitoring, and maintenance, and provides readers with a deep understanding of the theoretical foundations and practical applications of these techniques.Featuring contributions from leading experts in the field, the reprint presents a collection of 11 papers that offer insights into state-of-the-art approaches in dam engineering. The chapters cover topics such as fuzzy logic, genetic algorithms, artificial neural networks, and support vector machines, and provide practical examples of how these techniques can be applied to solve real-world dam engineering problems.Whether you are a researcher, engineer, or student in the field of dam engineering, “Soft Computing and Machine Learning in Dam Engineering” provides a valuable resource for staying up-to-date with the latest techniques and approaches in the field.

  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
dams; Polynomial Chaos Expansion; random fields; random forest; vibration analysis; gravity dams; safety assessment; probabilistic analysis; parameter uncertainty; sample optimization; variance-based sensitivity analysis; sensitivity analysis; polynomial chaos expansion; uncertainty; deep neural networks; rockfill dams; anomaly detection; machine learning; support vector machines; random forest; one-class classification; concrete dam; machine learning methods; structural behaviour; sensitivity analysis; model validation; ice loads; concrete dams; back-calculation; dam safety; monitoring; arch dams; seismic safety; endurance time analysis; non-linear seismic analysis; concrete damage model; tensile and compressive damage; design variable; finite element; feasibility design; surrogate; gravity dams; AutoML; roller compacted concrete (RCC); risk-informed design; Cascadia subduction zone (CSZ); non-linear structural analysis; concrete dam; multilayer perceptron neural network model; structural health monitoring; threshold definition; moving average of the residuals; moving standard deviation of the residuals; DBSCAN; n/a

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