Soft Computing and Machine Learning in Dam Engineering

• Kolejni autorzy: Hariri-Ardebili, M. Amin (Redaktor), Salazar, Fernando (Redaktor), Pourkamali-Anaraki, Farhad (Redaktor), Mazzà, Guido (Redaktor), Mata, Juan (Redaktor)
• Format: Elektroniczne Rozdział
• Język: angielski
• Wydane: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Hasła przedmiotowe: 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
• Dostęp online: 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, construc...