Practical ANN prediction models for the axial capacity of square CFST columns
Abstract
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 algori...thm 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.
Keywords:
Compressive strength / Machine learning / Levenberg–Marquardt / Bayesian regularization / Empirical equations / CFST columnsSource:
Journal of Big Data, 2023, 10Publisher:
- Springer