In this study, the diagonal compression behavior of cement-clay interlocking hollow brick (CCIHB) masonry walls is investigated using a combination of experimental and analytical study. The four wall configurations on full scales are considered: unreinforced-ungrouted, grouted, reinforced and grouted with vertical reinforcement. Diagonal compression tests were conducted as per ASTM E519 to measure in-plane shear strength, stiffness characteristics and failure modes. An analytical macro-modelling framework consisting of finite element plate representations for the masonry material, material zoning to simulate grout infill and embedded elements to simulate the vertical reinforcement. Analytical predictions are validated based on comparison of load vs displacement response, stress-based failure indicators, shear stress distribution, and global deformation patterns. The analytical results showed good agreement with experimental observations with peak load values predicted within 5% to 8% of measured values. Incorporation of grout infill increased the diagonal shear capacity by about 95% compared to ungrouted configuration and vertical reinforcement increased post-cracking resistance and deformation capacity. The combined grouted and reinforced wall system was shown to have the highest strength, stiffness, and ductility. Stress distribution patterns derived from the analytical models were found to be in qualitative agreement with those experimentally observed crack initiation and failure modes. The results validate the important role of the grout and reinforcement in enhancing the in-plane shear performance of CCIHB masonry walls and demonstrate the suitability of the proposed macro-modelling approach to capture their global structural behavior. This study forms a basis for the validated analysis in the assessment and design of interlocking masonry wall systems in sustainable and modular construction applications.
This research provides a broad management-oriented inquiry into the thermal buckling and stability behavior of hybrid nanocomposite-strengthened annular plates inside an auxetic elastic medium. The structural arrangement is treated via a multi-scale hybrid laminated nanocomposite (MHLN) scheme together with a higher-order shear deformation theory (HSDT) so it can properly catch the transverse shear effect and those thickness-dependent changes. Thermoelastic stress-strain relations are used too to see how thermal loading nudges the nonlinear stability properties of the ring-like layout. On top of that, the nearby auxetic substrate is modeled with the Haber-Schaim elastic foundation approach, which helps in a more realistic sense of how the negative Poisson's ratio actually boosts stiffness and also how it improves post-buckling resistance. The governing equilibrium equations are discretized and handled numerically by using the differential quadrature method (DQM) built on a Chebyshev-Gauss-Lobatto grid distribution, which gives strong computational efficiency and reliable numerical convergence. Parametric studies are then run to look at how foundation coefficients, temperature changes, nanoparticle dispersion, lamination sequence, and geometric ratios influence the critical buckling temperatures and overall structural stability. What comes out is a useful management scaffold for best design choices, thermal reliability checking, and stability regulation of advanced nanocomposite plate structures, for aerospace, mechanical, and energy engineering use cases.
The dynamic stability of structural systems reinforced by nanocomposites under dynamic loads have become increasingly important because of their multifunctional capabilities as a result of carbon nanotube (CNT) reinforcement. The present study provides an analytical framework for evaluating and optimizing dynamic stability of CNT-reinforced structural members using first-order shear deformation theory (FSDT). The transverse and shear strains have been assumed to have a linear relation to their respective displacements so that the effects of shear deformation can be accurately determined; this is very important for structural members that are of medium thickness. The constitutive behavior of the material will be based on Hooke's law using the effective elastic properties of CNT-reinforced nanocomposites derived through micromechanical homogenization techniques. The equations of motion are established through Hamilton's principle, yielding a variationally consistent formulation of the kinetic, potential, and work contributions for the system. In order to obtain closed-form solutions, the displacement fields are represented as a double trigonometric series in the context of the classical Navier approach, while also meeting simply supported boundary conditions at each end connection. The resulting eigenvalue problem establishes the critical dynamic stability boundaries, as well as principal parametric resonance states for the system. The parametric studies emphasize that important contributions to dynamic response and stability margins arise from the CNT volume fraction, the distribution of the CNTs, and the geometry of the structure. Through optimization analysis, the results show that using a specifically tailored arrangement of CNTs will significantly increase the stiffness of the structure, extend the time before instability occurs, and reduce the sensitivity of the structure to the excitation frequency/ampitude. This new method provides a sound analytical basis for the design and structural management of advanced CNT reinforced components for engineering applications where lightweight/high-strength/ stable materials are needed.
A semi-analytical framework has been outlined in order to determine optimal costs for both graphite enhance composite structures and to manage the fabrication/production cost reduction of each composite component subjected to an outside/acting shock load (dynamic). The mechanical behavior of the composite (the composite composite system) will be simulated/characterized by means of Halpin-Tsai theory to find out what the effective material properties will be based upon the shape/geometry of the graphene nanoplatelet (GPL) within the composite (shape). Structural outputs will be computed using first-order-shear-deformation-theory (FSDT) in order for each composite block of the frame to accurately reflect transverse shear effects that result from moderate thicknesses of the plates under load (weight). Hamilton's principle will steer the development of the governing equations of motion for each of the composite blocks to develop analytical representations (the 2D composite) of their (combination) response when they are loaded with a transient shock/impact. Coupled partial differential equations will be solved using the differential quadrature method (DQM) semi-analytically to provide the highest degree of accuracy, while also minimizing the amount of computational effort through reduced discretization methods required to solve them. Finally, time-dependent shock loading or transient response solutions will be obtained using the Laplace transformations. This work is unique because it integrates geometric optimization of the nanoplates with a manufacturing cost-reduction assessment to find the best performing solutions with the least manufacturing cost.
This study presents a novel, comprehensive analysis of reinforced composite structures using analytical, experimental, and statistical methods. A multi-scale prediction platform for fibre-reinforced composite materials is investigated, incorporating micromechanics, fatigue life prediction, and tribological behaviour analysis. It encompasses optimized Halpin-Tsai equations with calibrated shape factors, progressive damage modelling, S-N curve fatigue analysis with R-ratio effects, and abrasive/adhesive wear mechanisms to enable precise property predictions from constituent material to laminate performance. The micromechanics module uses sophisticated shape factors (zeta=1.0 for transverse modulus, zeta=0.5 for shear modulus) which eliminate systematic overestimation errors from traditional formulations. Fatigue analysis covers S-N curve modelling with high sensitivity to mean stress, environmental adjustments for temperature and moisture, and progressive damage accumulation. Wear module simulates both adhesion and abrasion mechanisms with material hardness sensitivity and environmental sensitivities. Full Monte Carlo uncertainty analysis yields 95% confidence intervals for all predictions. Experimental validation against literature data demonstrates excellent accuracy with R-2 > 0.90 for all the models: fatigue (R2= 0.914), wear (R-2=0.998), and micromechanics properties (R-2 > 0.99). The integrated system possesses 3.7 % mean prediction error, realistic fatigue life predictions with up to 3 & times;enhancement for fully reversed loading, and convenient wear life predictions in engineering time scales. The validated framework enables rapid composite design cycles with fewer experimental test requirements and high-fidelity predictions that are relevant to aerospace, automotive, and renewable energy applications where durability is an issue.
Management of nonlinear wave propagation in graphene-reinforced solar cells is an important part of their performance improvement and reliability. In this paper, the authors examine the nonlinear phase velocity characteristics of graphene-reinforced composite materials in the case of micro-sized solar cell plates. They use a new deep neural network-genetic algorithm (DNN-GA) combination to check the nonlinear wave propagation results, thereby increasing the prediction's accuracy and efficiency. The Halpin-Tsai model is used for determining the effective properties of graphene-reinforced composites, whereas the modified coupled stress theory (MCST) and sinusoidal shear deformation theory (SSDT) are combined to reflect the influence of microstructural behaviors on wave propagation. Modified pair stress theory (MPST) is also used to allow for the size-dependent and microstructural deformation effects of the composite material. The governing equations are obtained through Hamilton's principle, and an analytical method is then used to find the solutions to these equations. The results reveal that graphene reinforcement has a significant effect on the phase velocity and that the proposed framework is very useful for accurately tracing the material's nonlinear dynamical states. The research has pointed out the fact that it is critical to resort to effective management methods so that the nonlinear wave propagation can be controlled in order to sustain the micro-sized solar cells' structural integrity and performance enhancement in real-life applications. Now, this framework has become a reliable instrument for designing and optimizing advanced graphene-reinforced composites, which are meant for the next generation of solar technologies.
Porosity in functionally graded materials (FGMs) arises during fabrication due to several factors, depending on the technique employed. The type of reinforcement used significantly influences the overall porosity percentage. The presence of porosity negatively affects the performance of FG structures. Consequently, this study focuses on conducting a thermal buckling analysis of FG porous plates using a refined shear deformation plate theory. This theory accounts for a quadratic variation of the transverse shear strains through the thickness and satisfies zero traction boundary conditions on the plate's top and bottom surfaces without relying on shear correction factors. Thermal loads were applied by varying the temperature uniformly, linearly, and non-linearly through the thickness. The problem was addressed assuming the plate to be simply supported at its ends. The rule of mixtures was used to estimate the material properties, and a porosity parameter was introduced to represent the equal distribution of porosity in the metal and ceramic mixture. The effects of volume fraction index, porosity fraction index, aspect ratio, and side-to-thickness ratio were investigated.
This research paper explores the development and characterization of three Ti-Ni-Cu shape memory alloys (SMAs), Ti50Ni40Cu10, Ti50Ni38Cu12, and Ti50Ni35Cu15, synthesized through powder metallurgy. We investigate the effects of various heat treatments, including solution treatment, annealing, and aging, on their microstructure and properties. Comprehensive analyses, including Differential Scanning Calorimetry (DSC), Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray spectroscopy (EDX), and percentage porosity measurements, were conducted. This study aims to study the influence of heat treatment processes on SEM morphology, EDX, DSC thermal transitions, porosity, and density of the SMAs. From the DSC results, it is evident that transformation temperatures increase with higher Cu content in the solution-treated, annealed, and aged samples. The percentage porosity and pore size increase with higher Cu content in all heat treatment processes, but the minimum percentage porosity and smallest pore sizes are observed in the annealed samples. SEM images confirm the presence of porosity and reveal the pore sizes. Optical microscopy shows that grain size increases with higher Cu content.
The study presents a rational approach for design and optimization of high-strength One-Part Geopolymer Concrete (OP-GPC) mixes for a targeted compressive strength of 70 MPa using Response Surface Methodology (RSM). The model generated through RSM showed significant statistical relationship between the selected input and output variables. The high-strength OP-GPC was designed with ground granulated blast furnace slag as primary binder and anhydrous sodium metasilicate as the solid activator. The RSM model established statistically significant relationships between design parameters and compressive strength, which were subsequently validated through laboratory experimentation. The optimized OP-GPC have achieved compressive strength in the range of 70 MPa-76 MPa at 28 day. The experiments showed that approximately 90% of the targeted compressive strength was achieved within initial 7 days. The mechanical performance of high-strength OP-GPC was measured in terms of flexural and split-tensile strengths which was measured at 7.37 MPa and 5.32 MPa at 28 days, respectively. The durability of high-strength one-part GPC was also measured which showed low permeable void content of 8.17%, sorptivity of 0.0010 mm/s1/2 and chloride penetration depth of 5.81 mm at 180 days that indicated superior performance relative to conventional concrete. The results demonstrated that the proposed RSM-based design approach enables the development of high-strength OP-GPC with superior mechanical and durability performance. The optimized high-strength OP-GPC is recommended for sustainable and high-performance structural applications, particularly where early-age strength development and enhanced durability under aggressive exposure conditions is also needed.
The current research examines the transient dynamic deflection response of graphene-reinforced composite structures when subjected to external forces. Analytical solutions are specifically provided for the vibration of a rectangular composite plate that is ultimately loaded and is based on the sinusoidal shear deformation theory (SSDT). The equations of motion are derived from Hamilton's principle, considering shear deformation and bending. A Fourier series expansion is applied to the system response analysis, which allows for the quick calculation of transient deflections by breaking the problem down into harmonic components. One of the major advancements of this study is the application of Laplace transform inversion via the modified Dubner and Abate formulation, which greatly improves both the accuracy and speed of solving transient dynamic problems in composite materials. The investigation of graphene's impact on damping, natural frequencies, and overall dynamic stability of the composite structure is done along with the critical insights into its performance at different excitation frequencies. The findings indicate that the vibrational damping of graphene-based composites is better than that of conventional materials and that they also exhibit different resonance behaviors, which can be advantageous for the East and West coast engineering applications. The analytical framework presented in this paper can predict the dynamic response of graphene-reinforced composite plates and thus help in the design of structural materials that are more robust and resilient under dynamic loading conditions.
Fused Deposition Modeling (FDM) is one of the popular technologies for 3D printing. One significant limitation associated with this technology is the poor mechanical strength of the printed material. In this study, natural fibers (kenaf) were used for nylon filament reinforcement, and were evaluated through mechanical and morphological analysis. Kenaf fibers were submitted to water retting and bleached with 6% sodium hypochlorite solution. Then, the fibers were silanized by 3-aminopropyl triethoxysilane solution before being incorporated with nylon beads using thermal extruder machine. The study groups consisted of control and 0.1%, 0.3%, 0.5%, and 1% kenaf fibers reinforced nylon groups. For morphological analysis, fibers distribution in the filament was assessed through digital microscopic images and FeSEM cross-sectional images. Filament diameter was evaluated using digital caliper. For mechanical analysis, compression and flexural strength tests were conducted on the study samples. Both microscopic and FeSEM analysis revealed fibers distribution parallel to filament extrusion direction except for the 1% kenaf fibers reinforced group that showed some irregular fibers distribution. Filament diameter was not significantly different among the study groups. Mechanical analysis showed that 0.5% kenaf fibers reinforced group was not significantly different from the control group while the rest of the experimental groups were lower than the control group in terms of both compressive and flexural strength. Although kenaf fibers reinforcement with nylon filament showed regular morphological outcome at concentrations of 0.5% and lower, only 0.5% concentration appeared to have no significant effect on the mechanical strength of the FDM printed material, while the other studied concentration showed decreased mechanical strength.
3D printing has revolutionized various industries as well as enriching different important sectors including medicine and dentistry. Fused Deposition Modeling (FDM) is one of the significant techniques for 3D printing which utilizes thermoplastic filament feedstock. Natural fibers have been integrated into composite filament to enhance mechanical and physical defects of the FDM printed materials. The aim of this study was to assess the impact of two types of chemical treatments on kenaf fibers reinforcing polyamide composite filament by evaluating their chemical and surface properties. Sodium hydroxide (NaOH) and sodium hypochlorite (NaOCl) were the two chemical treatment solutions where kenaf fibers was processed at 6% concentration of each solution. Then, the fibers were compounded with nylon beads to form FDM filaments. Three groups of specimens were printed by FDM printer which were control, NaOH-treated, and NaOCl-treated. The samples were characterized by FTIR and evaluated for their surface hardness and surface roughness. The FTIR results showed that NaOH treatment was not as effective as NaOCl treatment in terms of eliminating lignin and hemicellulose. Surface hardness and surface roughness demonstrated minor improvement for the NaOCl-treated group compared to NaOH-treated group. It is recommended to rely on NaOCl treatment at 6% concentration with treatment time not exceeding 24 hours.
This study investigates the dynamic behaviour of nonlocal thermoelastic solid with diffusion subjected to a normal source with the focus on the effect of angular frequency on the medium. The governing equations are solved in the frequency domain. Numerical inversion technique is applied to find the solution in physical domain using Matlab. The obtained results are depicted graphically. As an application we use concentrated normal force, uniformly distributed and linearly distribued sources. We find that angular frequency significantly effects the components of normal stress, shear stress, mass concentration and temperature change. The results provide valuable insight for applications in advanced materials science, micro and nano-scale engineering, and dynamic load analysis.
Dynamic stability management within multifunctional composite systems is vital for the development and structural reliability of engineering applications. The study focuses on Cu-Ni carbon composite structure management with an integrated framework of micromechanical modeling, higher-order shear deformation theory, and physics-informed neural networks (PINNs). Effective material properties are modeled by modified Halpin-Tsai models, allowing improved management of constituent interactions between Cu-Ni matrix and the carbon reinforcements. The equations of motion are formulated in accordance with Hamilton's principle and Hooke's law, establishing and maintaining a consistent variational formulation with three independent components for displacement. It is recognized that as substructural interactions are more effectively managed, the elastic foundation can consist of Winkler's and Pasternak's coefficients, incorporating both normal and shear-layer contributions. Higher-order shear deformation theory is applied to properly characterize the stress-strain state during representation, eliminating the need for shear correction factors, permitting better predictive management of moderately thick plates. A PINN-based solution procedure is developed in which the governing partial-differential equations, along with the boundary values called upon during learning, are embedded within the learning process. The machine learning framework allows efficient use of resources with the potential for more robust accuracy in predicting stability boundaries, critical buckling loads, and vibration responses. The comparison studies show that the proposed procedure offers advantages over an existing and historical finite element model. The results of the studies also illustrated that PINNs offered more effective predictive management of composite dynamic stability and represented a hybrid of material modeling, structural theory, and machine learning. Hence, this work contributes to the continuing advancements of materials development by providing a promising platform for the next generation of multifunctional composites.
This study presents an analytical investigation of the free vibration behavior of functionally graded carbon nanotube-reinforced composite nanobeams under hygro-thermal environments. The reinforcement of carbon nanotubes within the isotropic polymer matrix is considered in four distribution patterns: one uniform and three functionally graded distribution types. The material properties of both the carbon nanotubes and the matrix are assumed to be temperature-dependent, and the effective properties are estimated using the extended rule of mixtures. The governing equations are formulated based on the refined shear deformation beam theory in conjunction with nonlocal elasticity theory and are analytically solved for simply supported boundary conditions. The analytical results are first validated against available literature, and then extensive parametric studies are conducted to explore the effects of geometric dimensions, temperature and moisture levels, the nonlocal parameter, and various beam theories on the vibrational behavior of nanobeams.
This paper analyzes the static response of power-law thick functionally graded plates (P-FGPs) using the refined Element-Free Galerkin (EFG) method. The C1 continuity requirements of the displacement field are accurately and effectively fulfilled. A method is also presented that eliminates the shear-locking phenomenon through the use of specific shape functions. The stretching effect is approximated using higher order shear deformation theory (HSDT), and the shear correction factor is not required. According to Reddy's power law rule of mixture, the Young's modulus and Poisson's ratio of the two-phase metal-ceramic membrane vary continuously through the thickness. Furthermore, a three-dimensional function based on machine learning is employed to estimate the central deflection. This study introduces a novel threedimensional estimating function for the central deflection of FGPs based on the results of the EFG method and sigmoid-cubic functions, representing the first application of this approach in the literature. Comparison with existing results demonstrates that the proposed estimation function provides an excellent fit to the response curve and is highly efficient for analyzing the static bending behavior of thick FGPs.
In this research the author implemented numerous investigations employing finite element analysis (FEA) in order to address the impact of the strengthening techniques with various shear reinforcement ratios on the maximum beam's capacity and their maximum deformations. The previous experimental study by the researchers focused on the development self-restoring mode to monitor the initiated cracks and stress. The performance of RC beams has been enhanced by internally injection ducts, beams strengthened by nitinol smart bars, and beams with a combination of both. They have studied using a three-dimensional (3D) nonlinear finite element (FE) model created by ABAQUS. The FE model incorporates geometric and material nonlinearities in concrete, steel, and Nitinol reinforcement, which is confirmed by comparing beams capacities and failure modes to published literature. The influences of two parameters: (a) strengthening configuration and (b) shear reinforcement ratio are investigated in complete parametric analysis comprising 12 models. Results indicated that The Finite Element model performance is equivalent to the experimental investigation utilized as a reference. The variances in terms of ultimate load capacity did not surpass 5%. when compared to experimental data. The proportion of ACI and FEM ultimate loads varied between 72 and 101 percent. Due to the criteria of safety as prescribed by the ACI to assure conservative design, FEM and the experimental ultimate loads have higher values compared to ACI analytical values.
The study aimed to evaluate the energy absorption behavior of cellular sandwich panels made of banyan wood skins and an aluminum honeycomb core under quasi-static loading conditions. The study analyzed two different setups: Type-I panels, which had banyan wood skin plates whose fibers are aligned in plane to the loading axis, and Type-II panels, which had skin plates whose fibers are aligned perpendicular to the loading axis. Both variants reliably aligned the aluminum honeycomb core with its cell axis parallel to the loading direction. An analysis was conducted on the behavior under quasi-static loading circumstances, and the capacities for absorbing energy were measured. The results showed that the energy absorption capabilities were improved during fibers along the cut (Type-I) situations in quasi-static circumstances. Type-I sandwich panels demonstrated exceptional effectiveness in absorbing impact energy, making them especially suitable for applications. Further interpretation of same is developed based on the application of machine learning algorithm. This algorithm considers the wood and aluminum properties and dimension to generate load v/s displacement behavior. The machine learning algorithm also shows the correlation of predicted data found is 99.92% with respect to actual. The algorithm best suits to find the behavioral pattern without conducting experimentation of specified sandwich panels in future application.
This paper develops a management approach to optimization of the structures that have been reinforced with recycled concrete aggregate (RCA) mixing ultrafine fly ash (UFA), and the analysis of vibration is the main concern. The analyzed structure is a plate, which rests on a Winkler-Pasternak elastic foundation, and this is done by applying higher-order shear deformation theory (HSDT), which aims at simulating the shear deformation effects in the system. To examine the vibration characteristics of the plate, a factorial design approach is employed to investigate the effects of different reinforcement ratios on structural behavior, through varying material combinations of RCA and UFA. The derivation of the governing equations of motion is done by using Hamilton's principle, which achieves a very thorough treatment of dynamic behavior, while taking into account both the material properties and the boundary conditions. For the solution of the equations, the differential quadrature method (DQM) with weighting coefficients and high-order derivatives is used, thereby guaranteeing a high level of accuracy in numerical solutions. Moreover, the Chebyshev-Gauss-Lobatto interpolation method is applied to the process of achieving the solution in order to further improve the accuracy of the solution by making the boundary conditions better approximated and by increasing the computational efficiency of the method. The experiment discloses that the dynamic response of the plate structure is greatly affected by the RCA and UFA, hence the study is able to suggest the best material combinations that would result in the least vibration amplitudes and the best structural performance. The optimization framework thereby sets up a good methodology for sustainable materials management in civil engineering applications, with the dual benefits of structures having better integrity and being environmentally friendly.
The rising demand for activated carbon (AC) in filtration, environmental protection, and energy storage necessitates cost-effective and sustainable production methods. Conventional AC production relies on non-renewable resources, leading to environmental concerns and high costs. This study addresses this gap by utilizing biowaste materials, specifically sawdust and walnut shells, for AC synthesis through chemical activation using phosphoric acid. The carbonization process was conducted at 300 degrees C, 600 degrees C, and 700 degrees C, followed by detailed characterization using Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), Proximate Analysis, and CHNSO analysis. Adsorption capacity was assessed using iodine value measurements, which identified 600 degrees C as the optimal temperature for activation. Carbon, hydrogen, Nitrogen, sulfur, and oxygen (CHNSO) analysis revealed that walnut shell-derived AC contained 36.5% more carbon than sawdust-derived AC, making it superior for energy storage applications. SEM analysis further confirmed a more heterogeneous structure with smaller pores in walnut shell AC, enhancing its adsorption efficiency. The study underscores the potential of biowaste-derived AC as a sustainable alternative, reducing reliance on conventional carbon sources and promoting circular economy principles. These findings contribute to waste valorization efforts, demonstrating an eco-friendly approach to producing high-performance AC while addressing environmental concerns associated with agricultural and industrial waste. Future research should focus on scaling production, optimizing activation processes, and exploring practical applications of biowaste-derived AC in industrial and environmental sectors to enhance its commercial viability.
