Study on multi-parameter optimization of seismic isolation bearings for continuous girder bridges considering interactions among key parameters.

Traditional isolation design for continuous girder bridges often focuses on single-parameter tuning, overlooking the complex interactions among yield strength, pre-yield stiffness, and post-yield stiffness. This paper proposes a multi-parameter optimization method to systematically investigate the nonlinear influence of each parameter on the seismic performance of bridges. First, using a conventional particle swarm optimization (PSO) algorithm, the individual and combined effects of each parameter on key response indicators are identified. On this basis, an adaptive particle swarm optimization (APSO) algorithm with dynamic inertia weights and learning factors is introduced to broaden the search space, expedite convergence, and reduce the likelihood of becoming trapped in local optima. Numerical studies indicate that, compared with the standard PSO method, APSO can reduce the total number of iterations by up to 40% while maintaining solution accuracy. The underlying mechanism is that APSO preserves particle diversity and dynamically adjusts the balance between global and local searches, thereby rapidly identifying the optimal bearing configuration. Compared with single-parameter or orthogonal design methods, the APSO-based multi-parameter optimization strategy significantly enhances structural ductility, as reflected by notable reductions in pier-top displacement and pier-bottom shear force. These findings underscore the robustness and efficiency of APSO in designing isolation bearings for high-dimensional problem spaces.