Defining parameters for concrete damage plasticity model

Bending test of seven reinforced concrete beams are modeled in finite element program to validate the modeling strategies by comparing the structural response of the beams. Three beams in the set are pre-damaged and strengthened with fiber reinforced composites before the bending tests. Cracks are implemented into the model by inserting geometrical discontinuities to represent the pre-damaged beams. Parametric variables such as crack width, length and interval are chosen to simulate different pre-damage levels. Once the proposed modeling strategies are validated by real experimental tests then 196 finite element models are created to study the effects of pre-damage levels on the moment capacity of reinforced concrete beams repaired with CFRP. Results indicate that inclusion of pre-damage levels by means of cracks into the cross sections have significant effect on beams moment capacity.

This study aims to investigate the mechanical behavior of cement mortar and concrete through a hybrid approach that integrates artificial intelligence (AI) techniques with finite element modeling (FEM). Support Vector Machine (SVM) models with Radial Basis Function (RBF) and polynomial kernels, along with Multilayer Perceptron (MLP) neural networks, were employed to predict the compressive strength (Fc) and flexural strength (Fs) of cement mortar incorporating nano-silica (NS) and micro-silica (MS). The dataset comprises 89 samples characterized by six input parameters: water-to-cement ratio (W/C), sand-to-cement ratio (S/C), nano-silica-to-cement ratio (NS/C), micro-silica-to-cement ratio (MS/C), and curing age. Simultaneously, the axial compressive behavior of C20-grade concrete was numerically simulated using the Concrete Damage Plasticity (CDP) model in ABAQUS, with stress–strain responses benchmarked against the analytical models proposed by Mander, Hognestad, and Kent–Park. Due to the inherent limitations of the finite element software, it was not possible to define material models incorporating NS and MS; therefore, the simulations were conducted using the mechanical properties of conventional concrete. The SVM-RBF model demonstrated the highest predictive accuracy with RMSE values of 0.163 (R² = 0.993) for Fs and 0.422 (R² = 0.999) for Fc, while the Mander model showed the best agreement with experimental results among the FEM approaches. The study demonstrates that both the SVM-RBF and CDP-based modeling approaches serve as robust and complementary tools for accurately predicting the mechanical performance of cementitious composites. Furthermore, this research addresses the limitations of conventional FEM in capturing the effects of NS and MS, as well as the existing gap in integrated AI-FEM frameworks for blended cement mortars.

The use of environmentally friendly materials is becoming increasingly important in order to increase sustainability and reduce carbon emissions in reinforced concrete structures. In this context, glass fiber-reinforced polymer (GFRP) bars, which are proposed as an alternative to traditional steel reinforcements, are attracting attention in engineering applications thanks to their advantages, such as high corrosion resistance, low weight, and electromagnetic permeability. However, the lower elasticity modulus of GFRP reinforcement compared to steel causes greater displacement and crack width under bending and shear effects, leading to certain limitations in structural performance. Due to the limited number of comprehensive analyses in the literature that simultaneously consider parameters such as reinforcement diameter, concrete strength, and stirrup spacing, this study aims to reveal the interactive effects of these parameters through numerical analyses and contribute to existing research. In this context, beam models using GFRP reinforcements with diameters of 10 mm and 12 mm, concrete strengths of 25 MPa and 40 MPa, and different stirrup spacings were analyzed using the ABAQUS (2022) software with a three-dimensional nonlinear finite element method.