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Lusong Yu,Yuxing Zhang,Li Wang,Qiren Pan,Yiyang Wen 한국강구조학회 2024 International Journal of Steel Structures Vol.24 No.1
Concrete-fi lled steel tubes (CFSTs) are widely used in engineering structures due to their excellent mechanical properties and economic benefi ts. This study focused on the construction of artifi cial neural network (ANN) models with high prediction capabilities and prediction accuracies that could predict the axial compression load capacities of short CFST columns using machine learning methods. A database was created by searching literature published over the past 40 years regarding circular-CFST bearing-capacity testing. Three ANN models with diff erent input parameters were developed, and used the Whale Optimization Algorithm to optimize the network weights and thresholds, the core idea of which comes from the humpback whale's special bubble net attack method. Then, the predictions of the proposed machine learning models were also compared with the theoretical values produced by the formulas proposed in existing codes. The results show that the ANN models had higher accuracies and a wider application range than the existing code models. Based on the Garson's algorithm, we perform parameter sensitivity analysis on the network model to enhance the interpretability of the neural network model. Finally, a graphical user tool is built to make the strength of CFST can be predicted quickly.
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