Cable Tension of Long-Span Steel Box Tied Arch Bridges Based on Radial Basis Function-Support Vector Machine Optimized by Quantum-Behaved Particle Swarm Optimization

Abstract

To investigate hanger force during the construction phase of large-span steel box tie arch bridges, the challenge of low accuracy in force identification due to multifactor coupling was addressed. An energy method was employed to derive formulas for calculating forces under different boundary conditions. Utilizing the QPSO-RBF-SVM machine learning algorithm model, predictions of bridge formation stage forces were conducted, integrating findings from actual engineering case studies. Error analysis on hanger force was performed, revealing that the quantum particle swarm optimization (QPSO) algorithm optimizes parameters in the radial basis function support vector machine (RBF-SVM). The model was trained on datasets, achieving an average relative error of 0.65% in predicted cable force values compared with measured values in the test set, with a coefficient of determination of 0.97. These results demonstrate superior accuracy compared with calculations derived from the energy method and other machine learning algorithms. This algorithmic model presents a promising approach for accurately assessing cable forces in large-span steel box tie arch bridges.