English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this study, a surrogate Machine Learning (ML)-based model was developed, to predict the load-bearing capacity (LBC) of concrete-filled steel square hollow section (CFSS) members, considering loading eccentricity. The proposed Artificial Neural Network (ANN) model was trained and validated against experimental data using the following error measurement criteria: coefficient of determination (R2), slope of regression, root mean square error (RMSE) and mean absolute error (MAE). A parametric study was conducted to calibrate the parameters of the ANN model, including the number of neurons, activation function, cost function and training algorithm, respectively. The results showed that the ANN model can provide reliable and effective prediction of LBC (R2 = 0.975, Slope = 0.975, RMSE = 294.424 kN and MAE = 191.878 kN). Sensitivity analysis showed that the geometric parameters of the steel tube (width and thickness) and the compressive strength of concrete were the most important variables. Finally, the effect of eccentric loading on the LBC of CFSS members is presented and discussed, showing that the ANN model can assist in the creation of continuous LBC maps, within the ranges of input variables adopted in this study.

Surrogate Neural Network Model for Prediction of Load-Bearing Capacity of CFSS Members Considering Loading Eccentricity