Physics-Informed Neural Network Framework for Predicting Creep-Induced Camber in Simply Supported Prestressed Concrete Girder Bridges

Camber in high-speed railway prestressed concrete (PC) girders increases with service time and affects profile control, ride comfort, and durability; reliable long-term midspan camber prediction is therefore required. Building on established hybrid physics–data modeling and discrepancy-correction ideas, we present a monitoring-oriented two-layer strategy for long-term camber prediction. In the physics layer, a physics-informed neural network (PINN) is formulated in a quasi-static, stage-aware manner to capture the physics-consistent low-frequency trend governed by creep, shrinkage, prestress loss, and staged loading. In the data layer, an XGBoost model learns a bounded, measurement-level residual correction from monitoring features to account for additional effects not explicitly represented in the physics layer, without altering the underlying physics-driven trend. The approach is evaluated using monitoring data from five 1:4 scaled specimens of a 24 m post-tensioned simply supported box girder and is compared against a theoretical calculation and a standalone PINN. Across prediction stages and specimens, the proposed strategy reproduces the measured camber evolution more closely than the benchmarks while preserving physically plausible trend behavior and yielding more consistent errors among girders. These results indicate that, under the present scaled-specimen and independently calibrated setting, a stage-aware physics baseline combined with bounded residual correction can provide closer agreement with the observed camber evolution than the benchmark models under sparse-monitoring conditions. Its engineering applicability can be repeatedly demonstrated across girders with different construction-condition combinations after girder-wise calibration.