Machine Learning Models for Inferring the Axial Strength in Short Concrete-Filled Steel Tube Columns Infilled with Various Strength Concrete
Keywords:machine learning, random forests, concrete-filled steel tube columns, data analytics
Concrete-filled steel tube (CFST) columns are used in the construction industry because of their high strength, ductility, stiffness, and fire resistance. This paper developed machine learning techniques for inferring the axial strength in short CFST columns infilled with various strength concrete. Additive Random Forests (ARF) and Artificial Neural Networks (ANNs) models were developed and tested using large experimental data. These data-driven models enable us to infer the axial strength in CFST columns based on the diameter, the tube thickness, the steel yield stress, concrete strength, column length, and diameter/tube thickness. The analytical results showed that the ARF obtained high accuracy with the 6.39% in mean absolute percentage error (MAPE) and 211.31 kN in mean absolute error (MAE). The ARF outperformed significantly the ANNs with an improvement rate at 84.1% in MAPE and 65.4% in MAE. In comparison with the design codes such as EC4 and AISC, the ARF improved the predictive accuracy with 36.9% in MAPE and 22.3% in MAE. The comparison results confirmed that the ARF was the most effective machine learning model among the investigated approaches. As a contribution, this study proposed a machine learning model for accurately inferring the axial strength in short CFST columns.
Authors who publish with Engineering Journal agree to transfer all copyright rights in and to the above work to the Engineering Journal (EJ)'s Editorial Board so that EJ's Editorial Board shall have the right to publish the work for nonprofit use in any media or form. In return, authors retain: (1) all proprietary rights other than copyright; (2) re-use of all or part of the above paper in their other work; (3) right to reproduce or authorize others to reproduce the above paper for authors' personal use or for company use if the source and EJ's copyright notice is indicated, and if the reproduction is not made for the purpose of sale.