AI-Driven Time-Variant Reliability Assessment of an Elevated Water Tank Over Its Service Life Using Radial Basis Function and Artificial Neural Networks

This study introduces a time-dependent reliability assessment of elevated reinforced-concrete water towers, highlighting the combined influence of material behaviour and environmental actions. The proposed framework integrates Kriging and Artificial Neural Networks within an active-learning ensemble, enabling a substantial reduction in computational effort while preserving the accuracy of Monte Carlo simulations. The analysis is based on a concrete crack-width limit state and explicitly incorporates long-term material effects, including concrete creep, as well as realistic wind conditions whose variability is adjusted according to projected climate-change scenarios. Relative humidity is also considered to capture its influence on creep development and serviceability performance. Results show a marked reduction in reliability under low-humidity conditions due to increased deformation, and a significant contribution of climate-induced wind variability to structural demand. Sensitivity analyses indicate that geometric parameters, particularly tank height and radius, play a major role in failure probability, whereas improvements in material properties help to reduce crack widths and enhance durability. A maintenance strategy based on a target reliability index is finally examined, demonstrating its effectiveness in improving performance when re-evaluated over the service life.