Mechanical Properties of SiC Nanoparticle-Reinforced Al-2024 Alloy

This study investigates the mechanical properties of Al-2024 alloy reinforced with SiC nanoparticles, highlighting the effectiveness of ultrasonic-assisted stir casting in achieving uniform dispersion of the nanoparticles. The aim is to enhance the material's inherent limitations in hardness and overall mechanical performance under demanding conditions by incorporating SiC nanoparticles. The experimental investigation explores varying SiC content (1%, 2%, 3%, and 4%) and its relationship with tensile strength and hardness. The results indicate a substantial 31% enhancement in hardness and a 25% improvement in tensile strength, demonstrating the effectiveness of nanoparticle reinforcement. Furthermore, several strengthening mechanisms were found to be important contributors to yield strength, including the Orowan mechanism, dislocation strengthening, and grain refinement strengthening. A maximum variation of 13% between the experimental and predicted yield strength of the Al2024-SiCnp composite confirms the reliability of the predictive models employed. Overall, the results support SiC nanoparticles' ability to improve Al-2024 composites' mechanical characteristics for cutting-edge engineering uses

Dynamic damage analysis of carbon fiber reinforced polymer composite pressure vessels

This study investigates spall damage and failure in Carbon Fiber-Reinforced Polymer (CFRP) pressure vessels under explosive internal loading using stimulated electric discharge. Analytical modeling, validation with published experimental data, and explicit numerical simulations were employed. A Coupled Eulerian–Lagrangian (CEL) framework in Abaqus/Explicit captured the dynamic-impact shock propagation, using continuum shell (SC8R) elements for the vessel, solid (C3D8R) for the PMMA insert, and Eulerian (EC3D8R) for copper-wire vapor. Intralaminar failure was modeled using the Hashin criterion, while interlaminar damage was captured using the energy-release-rate-tuned Virtual Crack Closure Technique (VCCT). Results demonstrated high-accuracy agreement with experiments in terms of free surface velocity and failure stresses, with minor discrepancies attributed to wire alignment, material model limitations, and wave reverberations. These findings highlight the reliability of the integrated modeling framework and support improved design and risk-mitigation strategies for composite pressure vessels, advancing safety and cost-efficiency through refined material characterization and structural assessment

Parameters Optimization for Manufacturing Advanced Self-Reinforced Composites based on Ultra-High Molecular Weight Polyethylene

In this study, the relationships between processing, structure and properties of self-reinforced ultra-high molecular weight polyethylene (UHMWPE) composites fabricated via thermal pressing are investigated. By systematically varying processing temperatures (145, 155, 165, 170, 175,180 °C) and pressures (25 and 50 MPa), we demonstrate that mechanical performance is governed by the interplay between fiber consolidation and the preservation of the oriented crystalline phase. Scanning electron microscopy reveals the presence of residual voids that are independent of the processing parameters, and which lead to interfacial failure and fibrillar fracture morphologies. We identify a critical processing threshold at 165 °C (25 MPa), which yields peak interlayer shear strength (7.8–11.1 MPa), bending strength (102–130 MPa), elastic modulus (23–42 GPa), and Charpy impact resistance (72–95 kJ/m²). Beyond this threshold, however, mechanical performance deteriorates due to fiber remelting and loss of anisotropy, resulting in the composite transitioning to an isotropic UHMWPE matrix. Conversely, elevated pressures fail to improve properties due to insufficient macromolecular interdiffusion, which is the dominant bonding mechanism. These findings establish a processing-structure-property framework for UHMWPE-based self-reinforced composites that balances interfacial adhesion and crystalline alignment, while providing actionable guidelines for engineering high-performance single-polymer materials

Experimental investigation on mechanical behavior of sandwich structures using Digital Image Correlation (DIC)

The aim of this work is to investigate the mechanical behavior of sandwich structures when subjected to edgewise and flatwise compression loadings, using 2D Digital Image Correlation (DIC). These structures are made of Glass Fiber Reinforced Polymer (GFRP) skins with polyurethane foam (PU) core. Initially, the mechanical characterization of each component within the sandwich structure is exanimated. Subsequently, flatwise and edgewise compression tests are conducted on the sandwich panels, in accordance with ASTM C365 and ASTM C364 standards, respectively. Different geometries are studied by testing various lengths of sandwich structures exposed to edgewise compression loads. The DIC technique is applied to analyze and comprehend the deformation and failure mechanisms of GFRP skins and sandwich structures. The results of the present study indicate that the flatwise compression test revealed condensation and densification of PU foam, accompanied by microcracks in GFRP skin. On the other hand, the edgewise compression test on sandwich structures with an equal length-to-width ratio identified several distinct failure modes, including skin-core debonding, shear sliding damage of the skin, and localized buckling. This localized buckling was initially observed in the mid-section of the specimens, followed by skin cracking on both sides, which then propagated across the width of the samples. For other geometric configurations of the sandwich structures, the Euler general buckling mode was observed. The results show that the length of samples has a significant effect on the collapse modes of sandwich structures under edgewise compression

Numerical and experimental analysis of mechanical and fatigue properties of special shaped 3D printed sample

Research in the field of property analysis of 3D printed structural elements raises many new questions. A major challenge is to understand the behavior of the material, as the raw material and the resulting printed sample cannot be considered the same in this respect. In 3D printing, the properties of the sample change due to high temperatures, changes in the state of the raw material and different setups. Currently, there is no standard for determining certain properties, which leads to the need for appropriate use of experimental and numerical tools. This study highlights the results of tensile testing and numerical analysis of a special 3D printed shape. Different material settings were used to allow a complete inverse analysis of the specimen behavior and calibration between experiment and model. The model was created in Ansys software and was prepared in several variations to be as close as possible to the real specimen. Subsequently, the numerical model was subjected to a simplified fatigue analysis with respect to the S-N curves and the predicted fatigue life of the specimen was determined

Mechanical behavior of fiber-glass plastic with hole pattern using digital image correlation and acoustic emission methods

In this paper, tensile tests of specimens with a pattern of holes made of fiber-glass plastic based on combined epoxy and phenol-formaldehyde resins are carried out in order to study the processes of damage accumulation and tension fracture. The Vic-3D video system is used to evaluate damage development and inhomogeneity of strain localization during loading. Continuous recording of acoustic emission signals is carried out during the tests, resulting in obtaining data on fracture mechanisms in the material. Ranges of peak frequencies are identified. Surface analysis of specimens was carried out using a microscope. A significant reduction in strength occurs due to the presence of a circular hole in the material, although additional holes do not exacerbate this effect. Fracture patterns of specimens with a hole pattern have been analyzed, and different "paths" of fracture have been observed. The comparison of strain fields obtained on the basis of application of three-dimensional digital optical system with the configuration of strain fields constructed as a result of numerical modeling by the finite element method has been carried out. It is found that the strain fields for different open hole patterns are quantitatively and qualitatively similar and identical

Civil Structural Health Monitoring and Machine Learning: A Comprehensive Review

In the past five years, the implementation of machine learning (ML) techniques has surged in civil engineering applications, particularly for optimizing and predicting solutions to various challenges.  More robust prediction models may be produced by combining test data collected in the laboratory or field with ML. These models may be used to estimate the compressive strength of masonry or repair mortars, probable damage scenarios in buildings, concrete models, beams, and columns for determining the mechanical characteristics of materials, damage detection in civil structures, and so on.  This comprehensive review aims to clarify the array of ML-based methods employed in civil engineering, specifically focusing on their efficacy in strengthening energy efficiency and cost-effectiveness. In combination with ML, the review explores corresponding soft computing methodologies such as fuzzy logic (FL) and design of experiments (DOE). A variety of case examples that highlight the versatility of these approaches, particularly in applications linked to structural reinforcement, enhance the story. The review navigates difficulties associated with the integration of soft computing in civil engineering and expands its scope to include emerging research directions. This synthesis of advanced artificial intelligence (AI) serves as a guide, providing new researchers with knowledge about a developing field. These methods could revolutionize the current situation by providing creative answers to complex problems that arise in civil structural applications

An interface-based microscopic model for the failure analysis of masonry structures reinforced with timber retrofit solutions

This paper presents a refined Finite Element (FE) modeling strategy for analyzing the failure behavior of regular masonry structures reinforced with timber-based retrofit solutions. The proposed model schematizes the masonry as brick units, modeled using two-dimensional linear elastic plane stress elements, mutually joined through zero-thickness cohesive interface elements. These interface elements serve to reproduce the nonlinear behavior of masonry because of the occurrence of failure mechanisms of the mortar joints. Reinforced timber frame elements are modeled using truss elements that exhibit elastic brittle fracture behavior. The interaction between the masonry sub-structure and the reinforced timber frame system is accounted for using special constraint conditions that simulate the mechanical behavior of anchorage connections. The reliability of the proposed model in reproducing the failure behavior of masonry is assessed through comparisons with experimental and numerical data available in the literature. Additionally, the efficacy of the retrofit technique based on timber frame structures is investigated in detail through pushover analyses on a two-story masonry wall representative of real-life masonry buildings. The results indicate that the proposed retrofitting strategy is an effective and eco-friendly retrofit solution to enhance the in-plane bearing capacity of masonry structures subjected to horizontal forces

Correlation coefficients of vibration signals and machine learning algorithm for structural damage assessment in beams under moving load

This paper presents a novel method of assessing structural damage in beams exposed to moving loads via acceleration signals through experimental studies. In this study, beams are supported on both ends, and their dynamic response to moving loads is assessed. The raw signal has been improved using a random decrement technique. Take measurements from different locations and calculate correlation coefficients between them, then use these as features to evaluate the structure. In order to create a reliable and potential framework for predicting damage efficiently, these features are used as input variables to the machine learning model. The proposed methodology exhibits promising results in accurately discerning and predicting damage in beam structure. It demonstrates a high level of precision to subtle changes in structural integrity when trained by machine learning on the statistical feature extracted from acceleration signals. As a result of this research, methods for detecting structural damage can be made more reliable and efficient by employing machine learning techniques. Additionally, structures operating in dynamic environments can benefit significantly from the proposed methodology

Fatigue behavior of pultruded fiberglass tubes under tension, compression and torsion

This work is devoted to an experimental investigation of fatigue behavior of pultruded fiberglass tubes under uniaxial tension, compression and torsion. Static tests were carried out; a presence of postcritical deformation stage during torsion is noted. Regularities of inhomogeneous strain fields evolution are analyzed using digital image correlation method. Fatigue curves are built for four cyclic loading modes: tension-tension, compression-compression, tension-compression and torsion. An analysis of specimens' fractures is carried out, typical damaging mechanisms are revealed. Residual dynamic stiffness data is obtained and studied using a previously proposed fitting model. Results demonstrate model's high descriptive capability and its flexibility to describe two-staged and three-staged stiffness degradation curves. An influence of loading mode on a shape of these curves is found out. Model parameters' dependence on maximum stress value during the loading cycle is studied using the Pearson's correlation coefficient. The necessity of multiaxial fatigue behavior investigation of pultruded fiberglass tubes is concluded