Time-Dependent Reliability and Sensitivity Analysis of Reinforced Concrete Bridges Considering Creep, Shrinkage, and Evolving Traffic Using Active Learning

This paper presents a comprehensive reliability and sensitivity analysis of RC bridges over time using an innovative approach that combines advanced techniques such as kriging and support vector regression. Leveraging real traffic data from the weigh-in-motion system, the study accurately models dynamic loads while accounting for the progressive increase in traffic weight and time-dependent phenomena such as creep and shrinkage. The proposed methodology enhances traditional reliability assessment, ensuring efficient failure probability evaluation while reducing computational costs. The analysis considers key random variables, including concrete strength, beam dimensions, permanent loads, axle characteristics, road load, and humidity, to estimate the probability of excessive deflection beyond serviceability limit state criteria. A sensitivity analysis quantifies the impact of each parameter on the failure probability over time. Applied to six RC bridges of varying spans (10, 15, 20, 25, and 30 m), this approach enables the identification of optimal maintenance strategies that balance structural safety with cost efficiency. The findings provide crucial insights for enhancing decision-making in bridge management and ensuring the long-term sustainability of road infrastructure.