Faculty Publications

Now showing 1 - 4 of 4

Strategy for refinement of nodal densities and integration cells in EFG technique
(Techno-Press, 2016) Bhavana Patel, V.S.S.; Narayan, B.K.S.; Venkataramana, K.
MeshFree methods have become popular owing to the ease with which high stress gradients can be identified and node density distribution can be reformulated to accomplish faster convergence. This paper presents a strategy for nodal density refinement with strain energy as basis in Element-Free Galerkin MeshFree technique. Two popular flat plate problems are considered for the demonstration of the proposed strategies. Issue of integration errors introduced during nodal density refinement have been addressed by suggesting integration cell refinement. High stress effects around two symmetrical semi-circular notches under in-plane axial load have been addressed in the first problem. The second considers crack propagation under mode I and mode II fracture loading by the way of introducing high stress intensity through line crack. The computational efficacy of the adaptive refinement strategies proposed has been highlighted. © 2016 Techno-Press, Ltd.
A novel EFG meshless-ANN approach for static analysis of FGM plates based on the higher-order theory
(Taylor and Francis Ltd., 2024) K P, A.; Swaminathan, K.; Indu, N.; H, S.
An Element Free Galerkin (EFG) meshless formulation and solutions using higher order shear deformation theory with nine degrees of freedom for the static analysis of Functionally Graded Material (FGM) plates are provided. This technique estimates the shape function using Moving Least Squares (MLS) method. The proposed method is validated by comparing the present findings with those in the literature. A novel Artificial Neural Network (ANN) model is developed to forecast the deflection of FGM plates within less computational time. Detailed parametric and convergent studies reveal that the proposed EFG solution and the ANN technique are more efficient than their conventional counterparts. The validation and comparison of the generated results in the present investigation with the other analysis methods revealed that the EFG method and ANN model give more accurate results than the FEM and other meshless methods. The current EFG-ANN model reduces computing time by 99.94% when compared to the EFG approach. Also, the accuracy is enhanced using the EFG approach with HSDT9 for the FGM plate. © 2023 Taylor & Francis Group, LLC.
EFG meshless-ANN approach for free vibration analysis of functionally graded material plates on elastic foundation in thermal environments
(Taylor and Francis Ltd., 2025) K P, A.; Swaminathan, K.; Hirannaiah, S.; Pavan, G.S.
This study focuses on free vibration analysis of functionally graded material (FGM) plates supported by Winkler–Pasternak elastic foundation in thermal environment using element-free Galerkin (EFG) meshless method. Plate kinematics depend on first-order shear deformation theory. Uniform, linear, and nonlinear temperature variations through the thickness direction are considered, along with the temperature-dependent material properties. The numerical outcomes obtained from EFG method are compared with those available in the published literature to validate the proposed method’s accuracy. An artificial neural network (ANN) model that can easily predict the natural frequencies of the plate is constructed from the EFG method outcomes. Further, the effect of foundation parameters, power law index, thickness ratio, temperature variations, and different boundary conditions are investigated; results show that these significantly influence the vibration response of FGM plates supported by the elastic foundation. Increasing the temperature of FGM plates supported by the Winkler–Pasternack foundation causes a decrease in the dimensionless fundamental natural frequency, and the uniform temperature influence is greater than that of linear and nonlinear temperature variation. The proposed EFG-ANN prediction model saves approximately 98.80% computation time when predicting the natural frequency with an accuracy of approximately 98.76% compared to that by EFG meshless method alone. © 2024 Taylor & Francis Group, LLC.
Fracture mechanics-based meshless method for crack propagation in concrete structures
(Elsevier Ltd, 2025) Paul, K.; Balu, A.S.; BabuNarayan, K.S.
Concrete is one of the most versatile construction materials, characterized by its high compressive strength and durability. It exhibits complex fracture behaviours in the non-linear region of the fracture process zone (FPZ) near crack tip, where micro-cracking, crack coalescence, and eventual macro-crack propagation occurs. Accurately predicting crack initiation and propagation in concrete structures is essential for ensuring their safety and performance. Traditional methods like finite element analysis (FEM) face challenges in capturing crack propagation due to the need for mesh refinement, which can be computationally expensive. This study aims to address this limitation by introducing the Element-Free Galerkin (EFG) method, which offers a more efficient approach for modelling crack behaviour in concrete beams. The maximum stress theory was used as the fracture criterion and the cohesive zone model (CZM) with a bilinear softening curve is employed to simulate the FPZ. Numerical examples of simply supported beam and cantilever beams with varying pre-notch positions and loadings were analysed. The results show that under axial and point loading, the stress intensity factor increases with crack length until unstable crack growth, leading to failure. The EFG method is found to be more accurate than FEM, particularly in regions with higher deformations, with a 13 % variation due to remeshing in FEM. Under point loading, EFG predicted deformation patterns with a 6 % variation in maximum deflection. This study demonstrates that the EFG-based model effectively predicts catastrophic failures, offering a computationally efficient solution for real-world concrete structures with pre-existing cracks or defects. © 2025 Institution of Structural Engineers

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