Abstract This study develops a novel artificial intelligence (AI)-based methodology to enable risk-based, long-term-focused decision support for bridge asset management. This methodology features three key modules: (1) a bridge condition simulation module integrating Markovian state transition dynamics, (2) a lifecycle cost analysis module to holistically quantify and aggregate the direct and indirect costs incurred due to bridge condition deterioration and intervention actions over a prolonged planning horizon, and (3) an AI-based decision-support module for optimal sequential maintenance actions at individual bridge level. The proposed methodology can flexibly accommodate the decision needs of different individual bridges within a bridge portfolio under various sources of uncertainty in near real time. Compared with traditional reactive, condition-based decision policies, the proposed AI-based policies are found to offer more proactive and adaptive decisions and to demonstrate superior performance in minimizing the life cycle costs. To further enhance risk-averse stakeholders’ trustworthiness in the AI-based decision policy, practical action constraints and decision reasoning are introduced, and the effect of action override is also investigated.
Abstract This study proposes a novel bonded straight iron-based shape memory alloy (Fe-SMA) plate system using hybrid anchorage for the shear strengthening of reinforced concrete (RC) T-beams and presents a comprehensive experimental evaluation of its structural performance. Unlike existing Fe-SMA shear-strengthening configurations that rely on mechanically complex anchorage or exhibit limited prestress transfer, the proposed hybrid-anchored configuration enables the reliable application of active prestressing without hydraulic anchorages while allowing flexible vertical or diagonal strengthening layouts. Fifteen specimens were tested under three-point bending to evaluate the effects of shear-span ratio ( λ = 1.0, 1.5, 2.0), activation temperature (150°C, 250°C), Fe-SMA inclination angle ( β = 90°, 45°), and bonding condition. The results demonstrate that the proposed system significantly improves the shear behavior of RC T-beams. Compared with unstrengthened specimens, activated Fe-SMA plates increased the maximum shear capacity by up to 43%, reduced internal stirrup stress, and enhanced postcracking stiffness by approximately 30%. The system also significantly improved crack-control performance, reducing the maximum diagonal crack width by more than 60% and increasing the initial cracking shear force by up to 116%, which effectively delayed crack propagation and mitigated brittle failure. Diagonal strengthening ( β = 45°), oriented perpendicular to the principal tensile stress direction, produced more uniform crack distributions and superior confinement. Bonded configurations provided additional advantages, ensuring a more uniform transfer of self-prestress and achieving shear capacity increases up to three times greater than those in unbonded specimens. The theoretical values closely matched the experimental results, confirming that the contribution of the activated Fe-SMA exceeded that of the stirrups by more than a factor of 2. Overall, the proposed bonded Fe-SMA strengthening system offers active crack control, substantial shear performance enhancement, and ease of installation, providing a practical and durable solution for upgrading existing concrete bridges and structural members.
Abstract To investigate the evolution of mechanical behavior in concrete box girder bridges during cantilever construction under the coupled influence of stochastic environmental temperature and humidity, a novel simulation method is proposed. This method integrates a temperature–humidity coupling analysis method for the internal fields of concrete and a constitutive model that accounts for multifactor interactions, including temperature-induced stress, drying shrinkage, creep, and the sensitivity of elastic modulus. The proposed method was applied to simulate and analyze the construction process of the Yuanhe Bridge in Jiangxi Province, China. Based on the statistical analysis of nearly two decades of daily historical meteorological data from the region, it was verified through fitting tests and statistical assessments that the probability distributions of temperature, humidity, and wind speed conform to Gaussian (normal) distributions. The results systematically reveal the statistical influences of stochastic environmental factors on the bridge’s camber, shear-lag effect, and prestress loss. The results indicate that the maximum camber and the shear-lag coefficient at the bottom slab–web junction exhibit slightly asymmetric distributions and can be well described by Bigaussian fitting curves, whereas the shear-lag coefficient at the top slab–web junction follows a Gaussian distribution. Increases in the daily temperature range and decreases in relative humidity are found to amplify the camber and the prestress loss. No discernible influence of wind speed on the camber, shear-lag effect, or prestress loss is observed. This study provides a crucial theoretical foundation and technical support for the refined control and optimization of cantilever construction of bridges under stochastic environmental conditions.
Abstract The splicing joint is the key load-transfer path for existing and newly added girders in widened bridges, yet its condition is difficult to quantify directly in practice. This study proposes the use of vehicle-induced quasi-static displacement transfer ratios to predict stiffness and local damage in the splicing joint. The widened bridge is modeled as dual girders of the Bernoulli–Euler type connected by distributed springs representing the splicing joint. Closed-form solutions are derived for the coupled system response under moving vehicle loads. The analysis shows that the displacement transfer ratio is governed by joint stiffness, which enables the joint stiffness to be directly identified from measured displacements. Low-pass filtering combined with time integration yields a stable and physically interpretable index for quantifying the joint condition, classified as weak, transitional, and rigid coupling. Furthermore, by using multiple measurement points together with a moving time window, the framework is extended to interval-based damage localization in the splicing joint. Numerical and scaled test results verify the effectiveness of the proposed method. The global index is highly sensitive to stiffness degradation, and the identified stiffness remains valid for varying vehicle masses and speeds, with deviations <5%.
Abstract Extreme floods pose significant threats to bridges, specifically due to hydrodynamic forces on inundated bridge superstructures and foundation scour, which can cause excessive lateral deflection at the deck level and instability of bridges. Such bridge damage produces immediate socioeconomic impacts on the society the bridges serve. The impact increases in a corrosive environment, where gradual corrosion deterioration combined with flood hazards creates a multihazard scenario. This study proposes a framework to estimate the resilience of degrading bridges subjected to randomly occurring flood events of varying intensities during their service life. The framework comprises four steps: flood hazard assessment, corrosion analysis, hydraulic analysis, and resilience assessment. A river-crossing bridge on the Mahi River in India was considered for demonstrating the framework using region-specific, real-life data. Finite-element analyses of the bridge at different degraded states were performed under various flow conditions resulting from flood events in the river. The obtained results demonstrate that bridge degradation, along with scour and inundation, causes increased flood vulnerability and decreased resilience of the bridge. A parametric study highlights the resilience’s sensitivity to key socioeconomic factors. Overall, research findings emphasize the need for further studies on various bridge types to enhance bridge safety during such multihazard scenarios.
