Wang Zhenyu, Zhou Jian, Peng Kang
School of Resources and Safety Engineering, Central South University, Changsha 410083, China.
Materials (Basel). 2024 Apr 25;17(9):1994. doi: 10.3390/ma17091994.
Concrete-filled double steel tubes (CFDSTs) are a load-bearing structure of composite materials. By combining concrete and steel pipes in a nested structure, the performance of the column will be greatly improved. The performance of CFDSTs is closely related to their design. However, existing codes for CFDST design often focus on how to verify the reliability of a design, but specific design parameters cannot be directly provided. As a machine learning technique that can simultaneously learn multiple related tasks, multi-task learning (MTL) has great potential in the structural design of CFDSTs. Based on 227 uniaxial compression cases of CFDSTs collected from the literature, this paper utilized three multi-task models (multi-task Lasso, VSTG, and MLS-SVR) separately to provide multiple parameters for CFDST design. To evaluate the accuracy of models, four statistical indicators were adopted (R, RMSE, RRMSE, and ). The experimental results indicated that there was a non-linear relationship among the parameters of CFDSTs. Nevertheless, MLS-SVR was still able to provide an accurate set of design parameters. The coefficient matrices of two linear models, multi-task Lasso and VSTG, revealed the potential connection among CFDST parameters. The latent-task matrix in VSTG divided the prediction tasks of inner tube diameter, thickness, strength, and concrete strength into three groups. In addition, the limitations of this study and future work are also summarized. This paper provides new ideas for the design of CFDSTs and the study of related codes.
钢管混凝土柱是一种复合材料承重结构。通过将混凝土和钢管以嵌套结构组合，柱的性能将得到极大提升。钢管混凝土柱的性能与其设计密切相关。然而，现有的钢管混凝土柱设计规范通常侧重于如何验证设计的可靠性，却无法直接提供具体的设计参数。作为一种能够同时学习多个相关任务的机器学习技术，多任务学习在钢管混凝土柱的结构设计中具有巨大潜力。基于从文献中收集的227个钢管混凝土柱单轴压缩案例，本文分别利用三种多任务模型（多任务套索、VSTG和MLS-SVR）为钢管混凝土柱设计提供多个参数。为评估模型的准确性，采用了四个统计指标（R、RMSE、RRMSE和 ）。实验结果表明，钢管混凝土柱的参数之间存在非线性关系。尽管如此，MLS-SVR仍能够提供一组准确的设计参数。多任务套索和VSTG这两种线性模型的系数矩阵揭示了钢管混凝土柱参数之间的潜在联系。VSTG中的潜在任务矩阵将内管直径、厚度、强度和混凝土强度的预测任务分为三组。此外，还总结了本研究的局限性和未来工作。本文为钢管混凝土柱的设计及相关规范的研究提供了新思路。
