Soft Computing and Machine Learning in Dam Engineering

• Other Authors: Hariri-Ardebili, M. Amin (Editor), Salazar, Fernando (Editor), Pourkamali-Anaraki, Farhad (Editor), Mazzà, Guido (Editor), Mata, Juan (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: Technology: general issues; History of engineering & technology; 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; one-class classification; concrete dam; machine learning methods; structural behaviour; 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; AutoML; roller compacted concrete (RCC); risk-informed design; Cascadia subduction zone (CSZ); non-linear structural analysis; multilayer perceptron neural network model; structural health monitoring; threshold definition; moving average of the residuals; moving standard deviation of the residuals; DBSCAN; n/a
• Online Access: DOAB: download the publication; DOAB: description of the publication

"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.