Doğan, Gamze2024-12-022024-12-0220232148-24462148-2446http://dx.doi.org/10.29130/dubited.1293075https://hdl.handle.net/20.500.13091/9190Corrosion damage, which can be considered a construction or service failure during the life of the structure, is an important parameter for structural elements. Strength loss due to corrosion is observed in reinforced concrete (RC) members and is an important parameter affecting the performance of the structure. Determining the shear strength of beams with corroded reinforcement is important in terms of strength loss, design, and reinforcement criteria in the structural member. In this context, data from 157 experimental tests of corroded RC beams reported in the literature were collected and the ultimate shear strength values of the beams were determined as a function of the test parameters. Strength estimation was performed using the machine learning regression algorithms XGBoost and AdaBoost. The results obtained were evaluated using the R2 , RMSE and MAE performance metrics and high estimation success was achieved. The study shows that with these systems, which can perform learning based on experimental data, it is possible to estimate the shear strength values of corroded beams with known production parameters without the need for experimental measurements.Basılı+Elektronikeninfo:eu-repo/semantics/openAccessMühendislik Temel Alanı>İnşaat MühendisliğiCorroded BeamShear StrengthMachine LearningXGBoostAdaBoostArticle10.29130/dubited.1293075