Practical ANN prediction models for the axial capacity of square CFST columns
نویسندگان
چکیده
Abstract In this study, two machine-learning algorithms based on the artificial neural network (ANN) model are proposed to estimate ultimate compressive strength of square concrete-filled steel tubular columns. The development such prognostic models is achievable since an extensive set experimental tests exist for these members. developed use simplest possible architecture but attain very high accuracy. A total dataset 1022 specimens with 685 stub columns and 337 slender subjected pure axial compression collected from available literature. This significant initial considering that field it falls under scope big data analysis. ANN validated by comparison results. validation study has shown superiority surrogate over Eurocode 4 design code. empirical equation derived best-tuned Bayesian regularization algorithm shows a better agreement results than those obtained Levenberg–Marquardt algorithm, similar conclusion applies independently. regularization-based negligibly slower one gives generalization even simplified ANN. Generally, besides its accuracy, key benefits presented applicability broader range other studies.
منابع مشابه
Study on Axial Compressive Behaviors and Bearing Capacity Formulas of the Composite CFST Columns
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ژورنال
عنوان ژورنال: Journal of Big Data
سال: 2023
ISSN: ['2196-1115']
DOI: https://doi.org/10.1186/s40537-023-00739-y