SENDJASNI, Sarra2026-04-292026-04-292026http://dspace.univ-chlef.dz/handle/123456789/2424A THESIS Submitted in partial fulfillment of the requirements for the degree of DOCTOR In Civil Engineering Option : StructuresThe objective of this thesis is to develop new predictive models for the compressive loadcarrying capacity of concrete columns confined using composite materials, either as internal reinforcements and/or external fiber-reinforced polymer (FRP) wraps, under various axial load eccentricity levels. To achieve this, general regression approaches and advanced machine learning models, such as XGBoost and Random Forest (RF), were employed. We also used deep learning models, such as BiLSTM and CNN-BiLSTM, to predict the axial compressive capacity of concrete-filled steel tubes confined with fiber-reinforced polymers. The proposed confinement models were developed using a bibliographic database composed of 308 reinforced concrete specimens strengthened with FRP bars, and 250 reinforced concrete specimens confined by external FRP wraps or steel tubes. The results showed that the proposed models, particularly those based on XGBoost and Random Forest (RF), achieved high accuracy with a coefficient of determination (R²) of 0.98, along with minimal values for the root mean square error (RMSE) and mean absolute error (MAE), thus outperforming conventional empirical models. The CNN-BiLSTM model also demonstrated better performance than the BiLSTM model. Furthermore, finite element analysis using ABAQUS showed that the predicted axial loads and deformations closely matched the experimental results, thereby confirming the accuracy and conservativeness of the finite element model employed.load-carrying capacityFRP barsFRP wrapssteel tubeThesis