Nguyen Hung Quang, Ly Hai-Bang, Tran Van Quan, Nguyen Thuy-Anh, Le Tien-Thinh, Pham Binh Thai
Thuyloi University, Hanoi 100000, Vietnam.
University of Transport Technology, Hanoi 100000, Vietnam.
Materials (Basel). 2020 Mar 7;13(5):1205. doi: 10.3390/ma13051205.
Concrete filled steel tubes (CFSTs) show advantageous applications in the field of construction, especially for a high axial load capacity. The challenge in using such structure lies in the selection of many parameters constituting CFST, which necessitates defining complex relationships between the components and the corresponding properties. The axial capacity (P) of CFST is among the most important mechanical properties. In this study, the possibility of using a feedforward neural network (FNN) to predict P was investigated. Furthermore, an evolutionary optimization algorithm, namely invasive weed optimization (IWO), was used for tuning and optimizing the FNN weights and biases to construct a hybrid FNN-IWO model and improve its prediction performance. The results showed that the FNN-IWO algorithm is an excellent predictor of P, with a value of R of up to 0.979. The advantage of FNN-IWO was also pointed out with the gains in accuracy of 47.9%, 49.2%, and 6.5% for root mean square error (RMSE), mean absolute error (MAE), and R, respectively, compared with simulation using the single FNN. Finally, the performance in predicting the P in the function of structural parameters such as depth/width ratio, thickness of steel tube, yield stress of steel, concrete compressive strength, and slenderness ratio was investigated and discussed.
钢管混凝土（CFSTs）在建筑领域有着优势应用，特别是具有较高的轴向承载能力。使用这种结构的挑战在于构成CFST的众多参数的选择，这就需要定义各组成部分与相应性能之间的复杂关系。CFST的轴向承载力（P）是最重要的力学性能之一。在本研究中，研究了使用前馈神经网络（FNN）预测P的可能性。此外，一种进化优化算法，即入侵杂草优化算法（IWO），被用于调整和优化FNN的权重和偏差，以构建混合FNN - IWO模型并提高其预测性能。结果表明，FNN - IWO算法是P的优秀预测器，R值高达0.979。与使用单一FNN的模拟相比，FNN - IWO的优势还体现在均方根误差（RMSE）、平均绝对误差（MAE）和R的精度分别提高了47.9%、49.2%和6.5%。最后，研究并讨论了在预测P时，结构参数如深宽比、钢管厚度、钢材屈服应力、混凝土抗压强度和长细比等函数的性能。
