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.

To evaluate the true load-bearing capacity and engineering reliability of a large span through a simply supported steel box arch bridge, a load test was conducted on the bridge. The example used in this test is the Jingchu Avenue Bridge located in Jingmen City, Hubei Province. Specifically, the static load test delineated six operational conditions, measuring parameters encompassing strain, hanger cable force, deflection, and potential cracks. The dynamic load test gauged the bridge’s dynamic response and various indicators, including pulse tests, vehicle tests, jump tests, and barrier-free vehicle tests. The findings indicated that the maximum measured strain values during the static load test surpassed the calculated values; nonetheless, the verification factors and relative residual strains adhered to the code requirements, and no cracks were detected. The dynamic load test unveiled that the measured frequency values exceeded the theoretical ones, the damping ratios were within the normal range, and the measured impact coefficients were lower than the values stipulated in the code, all of which were in conformance with the code requirements. The data obtained from this experiment can be utilized to refine the long-term maintenance plan for the bridge, especially as it holds considerable value for structural health monitoring and aging assessment.