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The purpose of this paper is to evaluate the effect of titanium oxide (TiO₂) and yttrium oxide (Y₂O₃) nanoparticles-reinforced pure aluminium (Al) on the mechanical properties of hybrid aluminium matrix nanocomposites (HAMNCs).

The HAMNCs were fabricated via a vacuum die-assisted stir casting route by a two-step feeding method. The varying weight percentages of TiO₂ and Y₂O₃ nanoparticles were added as 2.5, 5, 7.5 and 10 Wt.%.

Scanning electron microscope images showed the homogenous dispersion of nanoparticles in Al matrix. The tensile strength by 28.97%, yield strength by 50.60%, compression strength by 104.6% and micro-hardness by 50.90% were improved in HAMNC1 when compared to the base matrix. The highest values impact strength of 36.3 J was observed for HAMNC1. The elongation % was decreased by increasing the weight percentage of the nanoparticles. HAMNC1 improved the wear resistance by 23.68%, while increasing the coefficient of friction by 14.18%. Field emission scanning electron microscope analysis of the fractured surfaces of tensile samples revealed microcracks and the debonding of nanoparticles.

The combined effect of TiO₂ and Y₂O₃ nanoparticles with pure Al on mechanical properties has been studied. The composites were fabricated with two-step feeding vacuum-assisted stir casting.

The external confinement provided by the fiber-reinforced polymer (FRP) sheets leads to an improvement in the axial compressive strength (CS) and strain of reinforced concrete structural members. Many studies have proposed analytical models to predict the axial CS of concrete structural members, but the predictions for the axial compressive strain still need more investigation because the previous strain models are not accurate enough. Moreover, the previous strain models were proposed using small and noisy databases using simple modeling techniques. Therefore, a rigorous approach is needed to propose a more accurate strain model and compare its predictions with the previous models.

The present work has endeavored to propose strain models for FRP-confined concrete members using three different techniques: analytical modeling, artificial neural network (ANN) modeling and finite element analysis (FEA) modeling based on a large database consisting of 570 sample points.

The assessment of the previous models using some statistical parameters revealed that the estimates of the newly recommended models were more accurate than the previous models. The estimates of the new models were validated using the experimental outcomes of compressive members confined with carbon-fiber-reinforced polymer (CFRP) wraps. The nonlinear FEA of the tested samples was performed using ABAQUS, and its estimates were equated with the calculations of the analytical and ANN models. The relative investigation of the estimates solidly substantiates the accuracy and applicability of the recommended analytical, ANN and FEA models for predicting the axial strain of CFRP-confined concrete compression members.

The research introduces innovative methods for understanding FRP confinement in concrete, presenting new models to estimate axial compressive strains. Utilizing a database of 570 experimental samples, the study employs ANNs and regression analysis to develop these models. Existing models for FRP-confined concrete's axial strains are also assessed using this database. Validation involves testing 18 cylindrical specimens confined with CFRP wraps and FE simulations using a concrete-damaged plastic (CDP) model. A comprehensive comparative analysis compares experimental results with estimates from ANNs, analytical and finite element models (FEMs), offering valuable insights and predictive tools for FRP confinement in concrete.

The boundary integral method (BIM) is very attractive to practicing engineers as it reduces the dimensionality of the problem by one, thereby making the procedure computationally inexpensive compared to its peers. The principal feature of this technique is the limitation of all its computations to only the boundaries of the domain. Although the procedure is well developed for the Laplace equation, the Poisson equation offers some computational challenges. Nevertheless, the literature provides a couple of solution methods. This paper revisits an alternate approach that has not gained much traction within the community. The purpose of this paper is to address the main bottleneck of that approach in an effort to popularize it and critically evaluate the errors introduced into the solution by that method.

The primary intent in the paper is to work on the particular solution of the Poisson equation by representing the source term through a Fourier series. The evaluation of the Fourier coefficients requires a rectangular domain even though the original domain can be of any arbitrary shape. The boundary conditions for the homogeneous solution gets modified by the projection of the particular solution on the original boundaries. The paper also develops a new Gauss quadrature procedure to compute the integrals appearing in the Fourier coefficients in case they cannot be analytically evaluated.

The current endeavor has developed two different representations of the source terms. A comprehensive set of benchmark exercises has successfully demonstrated the effectiveness of both the methods, especially the second one. A subsequent detailed analysis has identified the errors emanating from an inadequate number of boundary nodes and Fourier modes, a high difference in sizes between the particular solution and the original domains and the used Gauss quadrature integration procedures. Adequate mitigation procedures were successful in suppressing each of the above errors and in improving the solution accuracy to any desired level. A comparative study with the finite difference method revealed that the BIM was as accurate as the FDM but was computationally more efficient for problems of real-life scale. A later exercise minutely analyzed the heat transfer physics for a fin after validating the simulation results with the analytical solution that was separately derived. The final set of simulations demonstrated the applicability of the method to complicated geometries.

First, the newly developed Gauss quadrature integration procedure can efficiently compute the integrals during evaluation of the Fourier coefficients; the current literature lacks such a tool, thereby deterring researchers to adopt this category of methods. Second, to the best of the author’s knowledge, such a comprehensive error analysis of the solution method within the BIM framework for the Poisson equation does not currently exist in the literature. This particular exercise should go a long way in increasing the confidence of the research community to venture into this category of methods for the solution of the Poisson equation.