A Transfer Learning-Based Hybrid Surrogate Modeling Framework for Efficient Multi-Objective Seismic Design of Long-Span Cable-Stayed Bridges

The performance-based seismic design of fluid viscous dampers (FVDs) for long-span cable-stayed bridges was fundamentally challenged by prohibitive computational costs and the lack of generalizable methodologies. This study proposed a transfer learning-based hybrid surrogate modeling framework for efficient multi-objective seismic design. High-fidelity models of three representative bridges were developed to generate a comprehensive seismic response database. A systematic comparison identified the Radial Basis Function Network (RBFN) as the optimal core surrogate model. The pivotal innovation was a transfer learning strategy, enabling a pre-trained RBFN model to be rapidly and accurately adapted to a new bridge design with minimal additional data. This adapted RBFN was integrated with a Kriging model to form a hybrid surrogate, which was embedded within an NSGA-II optimization loop to efficiently identify the Pareto-optimal set of FVD parameters. The robustness and performance gains of the optimized designs were rigorously validated through high-fidelity simulation. The proposed framework reduces the computational cost of the design cycle by approximately two orders of magnitude (from 1700 to 50 CPU-hours), providing a practical and reusable pathway for the seismic design of long-span cable-stayed bridges.