Calculation Method of Bearing Capacity of Elliptical Concrete-Filled Steel Tubular Short Columns under Axial Compression Based on Neural Network

Elliptical concrete-filled steel tubes (CFST) are structural elements where concrete is embedded in an elliptical steel tube, offering significant advantages such as effective axial compression resistance. These structures are widely applied in civil engineering and critical infrastructure like airport terminals and bridges. While most CFST research focuses on circular and rectangular cross-sections, there is limited study on elliptical CFST. The emergence of neural networks offers a novel approach to calculate the compressive capacity of elliptical CFST. Neural networks can learn from sample data without predefined assumptions, uncovering nonlinear relationships between the data and results. Furthermore, they can predict new variables, overcoming the constraints of traditional experimental and finite element methods. This paper proposes an advanced calculation method to quickly and accurately assess the axial compressive bearing capacity of elliptical CFST short columns. The model integrates neural network capabilities with unified strength theory, simulating the bearing capacity of elliptical CFST stub columns. Through simulation training, the method is validated for calculating the compressive performance of elliptical CFST. The findings demonstrate that this approach not only enhances computational efficiency but also provides a reliable and optimized tool for structural evaluation. The calculation efficiency of the compressive capacity of elliptical CFST based on a neural network is increased by about 23.7%.