To investigate the controllability of sensitive cable forces during the construction phase of cable-stayed bridges, a novel optimization method is proposed, based on BP neural networks, which combines Gaussian process prediction with a simulated annealing-optimized particle swarm algorithm to determine the tolerance intervals of construction cable forces. Based on the analysis results of multiple linear regression, the variables for optimization are identified, and a mapping relationship between the sensitive cable forces and displacement values is established using a BP neural network. Subsequently, a Gaussian process model is constructed to delineate the relationship between cable forces and reliability, with a focus on the reliability of displacements during the construction phase of the cross-section, specifically targeting sensitive cable forces. Finally, a combination of the simulated annealing algorithm and the particle swarm algorithm is employed to optimize the tolerance intervals of the cable forces. To validate the effectiveness of the proposed optimization method, a case study is conducted on the tolerance interval optimization of cable forces using a three-tower steel box girder cable-stayed bridge. In this study, the construction cable forces are treated as optimization variables, while the reliability of displacements at both the main girder section and the tower’s top section serve as the optimization objectives and constraint conditions. Under the premise of ensuring structural reliability, the accurate tolerance range for the stay cable forces during the construction phase of the cable-stayed bridge is obtained. The results indicate that the traditional PSO algorithm stabilizes after 26 iterations, whereas the hybrid intelligent algorithm reaches stability after just 13 iterations. In addition, the hybrid algorithm shows a significant increase in the objective function value during early iterations, demonstrating stronger optimization capability. This indicates that the optimization method exhibits better convergence and superior global optimization capability. It effectively improves the compatibility and controllability of the cable-stayed bridge construction process while simplifying the computational process.
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