Climate change poses escalating risks to bridge infrastructure, with short-term hazards–such as flash floods, scour, snowfall, wildfires and windstorms–interacting with long-term stressors like corrosion and thermal effects to compromise safety and functionality. The paper synthesises interdisciplinary research on these challenges, and highlights actionable adaptation strategies to enhance resilience at both asset and network levels. Two critical yet often overlooked dimensions in resilience-based bridge management are emphasised: the unique challenges of adapting heritage bridges, and the integration of human-centered approaches. These dimensions, supported by emerging digital technologies such as digital twins, IoT-enabled monitoring and AI-driven predictive tools, contribute to both the resilience and social sustainability of bridge infrastructure. By integrating technical, cultural and social considerations, the paper provides a foundational perspective for rethinking current design, preservation and maintenance practices, and for advancing infrastructure that is not only resilient to physical stressors but also socially sustainable amid accelerating climate challenges.

Transition zones such as level crossing and bridge approaches are critical links in railway networks due to higher degradation rates and maintenance needs. In this context, parametric optimization has been applied to improve the design in transition zones; however, it requires a more computationally efficient tool to support repetitive function evaluations, since the involved vehicle–track dynamic simulations are becoming more expensive to evaluate. For this purpose, a surrogate-based simulation methodology is proposed to search for an optimal combination of parameters relevant to the geometry and elasticity of track structures. Specifically, the presented methodology integrates finite element (FE)-based modeling with surrogate-assisted optimization: (1) the FE model is developed to characterize the dynamic behavior of a level crossing under a moving vehicle; (2) the optimization problem is formulated upon this mechanical model by extending the expensive FE simulations to an adaptive surrogate modeling scheme. This integration facilitates efficient exploration of the track design space (thereby reducing the computational cost), and a reasonable balance can be achieved between solution quality and computational effort. The methodology is applied to a Dutch railway case. Results show that compared to a reference design, the optimized design significantly improves performance indicators relevant to wheel–rail contact forces and energy dissipation in the ballast layer. The solution brings great potential in achieving a more desirable vehicle–track interaction and improving the connecting performance between level crossings and transitions. The methodology is applicable to other railway structures and may also contribute to improvements in current track design practices.

The use of sensitivity analysis is essential in model development for the purposes of calibration, verification, factor prioritization, and mechanism reduction. While most contributions to sensitivity methods focus on the average model response, this paper proposes a new sensitivity method focusing on the extreme response and structural limit states, which combines an extreme-oriented sensitivity method with polynomial chaos expansion. This enables engineers to perform sensitivity analysis near given limit states and visualize the relevance of input factors to different design criteria and corresponding thresholds. The polynomial chaos expansion is used to approximate the model output and alleviate the computational cost in sensitivity analysis, which features sparsity and adaptivity to enhance efficiency. The accuracy and efficiency of the method are verified in a truss structure, which is then illustrated on a dynamic train–track–bridge system. The role of the input factors in response variability is clarified, which differs in terms of the design criteria chosen for sensitivity analysis. The method incorporates multi-scenarios and can thus be useful to support decision-making in design and management of engineering structures.

The present study compared the effectiveness and cost-effectiveness of four pavement treatments, including hot in-place recycling, milling and filling, thin HMA overlay and microsurfacing. The multiple regression analysis was employed to investigate the effectiveness of treatments and the effect of pretreatment rutting severity and traffic conditions on maintenance effectiveness. The rutting depth (RD) was selected as a performance indicator. The reduction of RD degradation rate and increase in average RD over monitoring period were used as measures of treatment effectiveness. Life-cycle cost analysis was performed to evaluate the treatment cost-effectiveness over a 50-year analysis period. Results indicate that the hot in-place recycling possesses the highest effectiveness and cost-effectiveness. Using reclaimed asphalt pavement (RAP) at appropriate maintenance timing substantially benefits for restoring the rutting resistance of asphalt pavement. These findings provide project agencies with quantitative evidence to support the establishment of the rutting-based maintenance decision-making system and the utilization of RAP in the sustainable pavement management strategies.

To evaluate the effectiveness of maintenance treatments for asphalt pavement, four types of treatments, including the hot in-place recycling (HIR), milling and resurfacing (M&F), thin HMA overlay (THO) and microsurfacing (MS), were analyzed. 8 highway segments, in total 491.74 km, in Jiangsu Province in China, were investigated and the data of rutting depth (RD) in their maintenance history was collected. Based on the data of RD, the effectiveness of four treatments was quantitatively compared by three indicators, namely, performance jump (PJ), deterioration rate reduction (DRR) and average deterioration reduction rate (ADRR). Moreover, the applicability of the proposed effectiveness analyses was assessed by laboratory rutting tests with field cylindrical samples. Results indicate that the HIR is the most effective treatment, followed by M&F and THO. While the MS is the least effective treatment, whose service life is only three or four years. The findings highlight the significance of the proper traffic opening time after maintenance to avoid the weakened rutting resistance caused by high-temperature mixtures, the effect of the RD before maintenance on maintenance decision-making, and the application of high-quality RAP in the timely maintenance activities. Therefore, the performance-based maintenance strategy should be establish to improve pavement performance and extend pavement service life. Further research on the pavement performance after RAP application is necessary.