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Efcient neural network‑ and tree‑based machine learning models for predicting shear capacity of RC slender walls
Tác giả: Sy-Minh Nguyen, Ngoc-Long Tran, Trong-Ha Nguyen, Van-Binh Tran, Duy-Duan Nguyen
3    0
Asian Journal of Civil Engineering
Quyển: 25     Trang: 3595-3609
Đường link/DOI: https://doi.org/10.1007/s42107-024-00998-x
Năm xuất bản: 3/2024
Cùng tác giả
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and fragility of nuclear power plant structuresBending strength diagnosis for corroded reinforced concrete beams with attendance of deterministic, random and fuzzy parametersSeismic fragility analysis of reinforced concrete piers of steel box girder bridges: A parametric studyInfluence of Squeezing Rate on Yield Stress and Viscosity of Fresh MortarA Site-Specific Response Analysis: A Case Study in Hanoi, VietnamEfficiency of flexural strengthening RC beams using fiber reinforced polymer materialsNonlinear seismic soil-structure interaction analysis of nuclear reactor building considering the effect of earthquake frequency contentReliability Assessment of Steel-Concrete Composite Beams considering Metal Corrosion EffectsA calibration of the material model for FRCPractical artificial neural network tool for predicting the axial compression capacity of circular concrete-filled steel tube columns with ultra-high-strength concreteIdentification of parameters in the crystal plasticity model 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