Capacity prediction of straight and inclined slender concrete-filled double-skin tubular columns

Allouzi, Rabab; Abu-Shamah, Awni; Alkloub, Amer

doi:10.1108/mmms-05-2022-0079

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…However, the authors did not provide sufficient information on data preprocessing. Allouzi et al [1] implemented a conventional approach using 3D nonlinear finite element (FE) model for predicting the behavior of concretefilled double-skin steel tube columns while Tran and Kim [61] successfully applied the ANN model with eight input parameters and one hidden layer with 17 nodes and the same associated activation functions (hyperbolic-tangent and pure linear), implemented on the same task. Tran et al [60] conducted a study with 145 tests of elliptical CFST columns under axial loading by establishing a combination of ANN and interior-point (IP) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Practical ANN prediction models for the axial capacity of square CFST columns

Đorđević

Kostić

2023

J Big Data

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In this study, two machine-learning algorithms based on the artificial neural network (ANN) model are proposed to estimate the ultimate compressive strength of square concrete-filled steel tubular columns. The development of such prognostic models is achievable since an extensive set of experimental tests exist for these members. The models are developed to use the simplest possible network architecture but attain very high accuracy. A total dataset of 1022 specimens with 685 stub columns and 337 slender columns subjected to pure axial compression is collected from the available literature. This is significant for the development of the initial model considering that for this field it falls under the scope of big data analysis. The ANN models are validated by comparison with experimental results. The validation study has shown the superiority of surrogate models over the Eurocode 4 design code. The empirical equation derived from the best-tuned Bayesian regularization algorithm shows a better agreement with the experimental results than those obtained by the Levenberg–Marquardt algorithm, and Eurocode 4 design code. A similar conclusion applies to stub and slender columns independently. The Bayesian regularization-based model is negligibly slower than the one developed on the Levenberg–Marquardt algorithm but gives a better generalization even with simplified ANN. Generally, besides its high accuracy, one of the key benefits of the presented ANN model is its applicability to a broader range of columns than Eurocode 4 and other studies.

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Section: Introductionmentioning

confidence: 99%