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

Tien-Thinh Le

doi:10.3390/app10103452

Abstract

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.

Highlights

Composite materials are very widely employed in the construction industry, due to their efficient structural performance and reasonable cost [1]
As the focus is on concrete-filled steel square hollow section (CFSS) members under both concentric and eccentric loading, the input data including the mechanical properties of steel and concrete, the cross-sectional width, column length, steel tube thickness, loading eccentricities at the top and bottom of the member, were collected from the available literature
A primarily statistical analysis of the database is shown in Table 1, including the min, average, max, standard deviation (StD) and coefficient of variation (CV) of all variables

Summary

Introduction

Composite materials are very widely employed in the construction industry, due to their efficient structural performance (high strength and ductility) and reasonable cost [1]. In this context, concrete-filled steel square hollow section (CFSS) members utilize the advantages of both steel and concrete. Concrete-filled steel square hollow section (CFSS) members utilize the advantages of both steel and concrete They comprise a steel hollow section of square shape, filled with plain concrete. They may be used as columns and beam-columns in high-rise buildings [2,3]. CFSS members could serve as beams in low-rise infrastructures (i.e., industrial buildings) [4]. CFSS members have become increasingly popular in structural systems, due to their favorable structural performance characteristics, including high strength and ductility, and easy beam-to-column connection manufacturing process [5,6,7,8,9,10]

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Methods

Results

Discussion

Conclusion