Scholarlink

<p>Concrete-filled steel tube (CFST) columns with ultra-high-strength concrete (UHSC) offer enhanced strength and stiffness, providing superior load-bearing capability and improved durability in construction. However, the high material cost and potential difficulties in manufacturing and handling UHSC can limit their widespread application. To overcome this problem, thispaperproposes a hybrid approach for the Axial Compression Capacity of Circular CFST with UHSC. The proposed hybrid technique is the joint implantation of theDual Transformer Residual Network (DTRN)-Arithmetic Optimization Algorithm (AOA). The primary aim of the proposed method is to improve the axial strength of circular concrete-filled steel tube columns. The DTRN is utilized to predict the load capabilityCCFSTcolumns. The AOA techniqueis employed to optimize the weight parameters of the DTRN. The proposed model is done in MATLAB compared to existing method like Artificial Neural Networks (ANN), Back Propagation Artificial Neural Network (BP-ANN), and Conditional Tabular Generative Adversarial Network (CTGAN). The existing method shows the root mean square error of1598 kN, 1588 kN, and 1578 kN and the proposed method shows the RMSE of 1569 kN, which is lower than the other existing method. At last, the experimental outcomesdisplay that the proposed technique can effectually and accurately find the ideal global solutions.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing Axial Compression Capacity of Circular Concrete-Filled Steel Tube Columns with Ultra-High-Strength Concrete

  • 期刊论文

  • 发表日期：

    2026年6月26日

查看全文
  • R. Rajesh Guna,
  • M. Mariappan,
  • S. Sutha Viji,
  • R. Sakthivel,
  • M. Chinnasamy

摘要

Concrete-filled steel tube (CFST) columns with ultra-high-strength concrete (UHSC) offer enhanced strength and stiffness, providing superior load-bearing capability and improved durability in construction. However, the high material cost and potential difficulties in manufacturing and handling UHSC can limit their widespread application. To overcome this problem, thispaperproposes a hybrid approach for the Axial Compression Capacity of Circular CFST with UHSC. The proposed hybrid technique is the joint implantation of theDual Transformer Residual Network (DTRN)-Arithmetic Optimization Algorithm (AOA). The primary aim of the proposed method is to improve the axial strength of circular concrete-filled steel tube columns. The DTRN is utilized to predict the load capabilityCCFSTcolumns. The AOA techniqueis employed to optimize the weight parameters of the DTRN. The proposed model is done in MATLAB compared to existing method like Artificial Neural Networks (ANN), Back Propagation Artificial Neural Network (BP-ANN), and Conditional Tabular Generative Adversarial Network (CTGAN). The existing method shows the root mean square error of1598 kN, 1588 kN, and 1578 kN and the proposed method shows the RMSE of 1569 kN, which is lower than the other existing method. At last, the experimental outcomesdisplay that the proposed technique can effectually and accurately find the ideal global solutions.