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.

Rapid and accurate prediction of seismic response and fragility for bridge pile-group foundations (PGFs) is crucial for assessing seismic resilience. However, the high computational cost of traditional high-fidelity nonlinear analysis limits the application of probabilistic seismic risk analysis. To address this, an integrated deep learning framework is proposed that employs a unidirectional, multi-layer LSTM network for end-to-end prediction of structural responses directly from ground motions. The proposed model features two innovations. First, its multi-output capability enables simultaneous prediction of complete response time histories and peak values for key engineering demand parameters—bending moment, curvature, and pile cap displacement. Second, the network incorporates sliding time windows and residual connections to capture complex nonlinear soil–structure interaction. These predictions are integrated into a probabilistic seismic demand model to generate fragility curves. The framework is validated using a high-fidelity OpenSees model of a real bridge PGF subjected to 1000 ground motions. Results demonstrate the model’s excellent predictive accuracy: for peak bending moment, the mean predicted-to-actual ratio ranges from 0.97 to 1.03, with standard deviation below 0.12; the derived fragility curves show excellent agreement with benchmarks, achieving an average R² of 0.985 across four damage states. More importantly, the framework reduces the time for a complete fragility assessment (200 incremental dynamic analyses) from approximately 12 h to about 1 s—a 40,000× speed-up—making data-driven rapid and large-scale seismic risk assessment a reality. The proposed framework provides engineers with a practical design tool for rapidly evaluating alternative foundation configurations and informing seismic design decisions, thereby integrating advanced data-driven methods directly into the engineering design workflow.

Given the distinct operational features of flex-route transit (FRT) compared to conventional fixed-route systems, this study integrates FRT with urban rail transit and proposes a collaborative optimization approach for feeder flex-route operations and scheduling. The model incorporates path planning, timetable design, and vehicle scheduling to minimize travel costs, enhance operational efficiency, and improve service quality. A mixed-integer nonlinear programming method is applied to optimize scheduling while aligning with rail timetables and passenger transfer behavior. Simulation experiments based on realistic urban transit scenarios are conducted to validate the model, optimizing key operational parameters such as departure intervals, vehicle deployment, and stop selection. The results demonstrate that the proposed approach effectively adjusts schedules and vehicle assignments to accommodate fluctuating passenger demand. This study offers both a theoretical foundation and a practical framework for integrated scheduling of feeder flex-route services, with potential applications for improving coordination and performance in multimodal public transit systems.

Rapid assessment of seismic capacity for bridge pile group foundations under seismic loads is critical. This study employs the second-order central difference method to explore seismic capacity sensitivity and identify critical parameters. Subsequently, 1000 Latin hypercube samples were taken for these parameters, and 1000 analytical models were built. The overall displacement ductility is selected as the capacity indicator, and seismic capacity analysis is conducted on the models to obtain a capacity indicator. A BP model was constructed with the critical parameters as inputs and a capacity indicator as outputs to predict capacity values. Then, a regression function between capacity indicators and critical parameters is fitted to establish a capacity value assessment (CVA) model. Finally, capacity indicators are predicted using both the CVA model and the BP model, and the prediction results are compared. Results indicate that the critical parameters are tensile strength of reinforcing steel, cohesion of soil, cross-sectional area of pile, pile spacing, and longitudinal reinforcement ratio. The BP model can effectively predict the capacity indicators. The computational results of the CAV model show good agreement with the predictions from the BP model, demonstrating the reliability of the assessment model. This study provides a novel approach for disaster prediction of bridge pile group foundations.

As populations age, public transit systems face increasing pressure to meet the specific mobility needs and service expectations of elderly passengers. To address these challenges, this study adopts a perception-oriented approach and uses a structural equation model (SEM) to develop an evaluation model for age-friendly public transit. This model integrates refined service attributes with psychological mediating factors and systematically examines how service attributes influence overall satisfaction through both direct and indirect (mediated) pathways. The SEM analysis reveals that three service factors—Adequacy of Age-Friendly Facilities, Service Professionalism, and Environmental Comfort and Convenience—have significant positive effects on overall passenger satisfaction. Meanwhile, two psychological factors—Perceived Safety and Perceived Social Support—emerge as significant mediators linking these service factors to overall satisfaction. Collectively, the findings suggest that improving service quality for elderly passengers requires not only improvements to physical infrastructure and service management, but also attention to psychological pathways related to a sense of safety and emotional connectedness. This underscores the critical role of psychological perceptions in shaping overall satisfaction. Based on these insights, the study outlines targeted, practice-oriented strategies to support future improvements in age-friendly public transit.