Inspection, experiment, and stochastic analysis on geometric stability, material deterioration, and structural performance of 50-year-old reinforced concrete beams: Case study of the Jialing River Bridge in an inland warm region

Assessing the long-term structural performance of aged reinforced concrete (RC) beams is critical for ensuring the safety of aging infrastructure. However, a research gap persists: scarce field-measured data on 50-year-old naturally aged in-service RC beams in inland China, paired with insufficient integrated uncertainty quantification based on performance-oriented models. To address this, this study investigates 50-year-old RC beams retrieved from the demolished Jialing River Bridge (Chongqing, China), provides valuable empirical data, and adopts a three-stage assessment framework: first, at the demolition site, discarded RC beams were selected for destructive disassembly inspection, including appearance dimensional measurements, sampling for concrete/steel reinforcement mechanical property tests, carbonation depth testing, free chloride ion concentration testing, and steel reinforcement mass loss rate testing; second, in the structural laboratory, four-point bending tests were conducted on field-retrieved RC beams to measure load–displacement curves and obtain yield moments as well as ultimate moments; third, the measured geometric/material parameters and their stochastic properties (after 50 years of service) were input into a finite element model (FEM), and the Monte Carlo simulation was applied to analyze uncertainty in the flexural behavior of RC beams. Key findings indicate that, under the subtropical humid monsoon climate of Chongqing (inland warm region of China), the beams exhibit negligible material degradation and maintain satisfactory serviceability and load-bearing capacity after 50 years of service; significant uncertainties exist in appearance, material properties, and steel reinforcement corrosion—leading to structural behavior variability, which is effectively quantified by probabilistic analysis via statistical distributions and confidence intervals to support rational reliability evaluation; despite overall mild steel reinforcement corrosion, its dispersion is high, with local severe corrosion likely to impair load-bearing capacity; additionally, concrete carbonation is identified as the dominant corrosion driver for this bridge, while extremely low free chloride content rules out chloride-induced corrosion. This work delivers rare inland field data and validates an “inspection-experiment-probabilistic analysis” framework, supporting maintenance decision-making for similar aging RC infrastructure.