Structural behaviour of corroded reinforced concrete beams: A review of experimental and numerical studies

Reinforced concrete (RC) beams are widely used in civil infrastructure because of their high load-bearing capacity. However, environmental exposure can facilitate the infiltration of corrosive substances, leading to reinforcement corrosion and progressive impairment of structural integrity. Steel corrosion reduces reinforcement mechanical properties, weakens the steel–concrete bond through expansion of corrosion products, and causes concrete cracking and degradation. These deterioration mechanisms reduce the load-bearing capacity and durability of RC beams. Numerous experimental and numerical studies have investigated the structural behaviour of corroded reinforced concrete beams (CRCBs) and different aspects of residual structural performance. Although corrosion-induced deterioration in RC beams has been widely studied, a comprehensive review focusing on structural behaviour and numerical modelling remains limited. This paper reviews peer-reviewed experimental and numerical studies on CRCBs, selected based on stiffness degradation, strength reduction, ductility loss, bond deterioration, failure modes, and finite element modelling approaches. The reviewed studies include both chloride-induced and carbonation-induced corrosion, although the literature is predominantly focused on chloride-induced deterioration. The compiled experimental data indicate moderate correlations between average corrosion level and residual yield and ultimate load capacities, with R 2 values of 0.51 and 0.53, respectively, suggesting that average corrosion loss alone cannot fully capture the structural response of CRCBs. Common numerical modelling approaches are also evaluated, with emphasis on concrete degradation, steel strength deterioration, pitting corrosion, and bond–slip relationships. The review highlights key factors governing structural performance, identifies discrepancies among published findings, discusses the limitations of existing modelling approaches, and identifies research gaps and recommendations for future studies.