Italian Group Fracture (IGF): E-Journals / Gruppo Italiano Frattura
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Behavior of steel columns with double curvature: a numerical simulation and design-oriented parametric study
Full text linkThis research present a novel investigation, which focuses on the numerical exploration of steel columns having a double curvature, built with both hollow square and circular cross-sections. A finite element model was initially created using ABAQUS software and was validated through a series of compression experiments conducted on square hollow specimens exhibiting double curvature. close agreement was observed in term of ultimate loads, load–displacement curves and deformed shapes corresponding to the failure modes. Based on validated numerical simulations, parametric analyses are carried out to investigate the effects of major geometric parameters on the axial bearing capacity of double curved steel columns. The study consists in a systematic variation of curvature angle (20°, 25°, 30°, and 35°), curvature radius (500 mm, 700 mm, 900 mm, and 1100 mm), square cross-section size (250 mm, 300 mm, 350 mm, and 400 mm), circular diameter (318 mm, 381 mm, 445 mm, and 509 mm) and end offset distance (400 mm, 600 mm, 800 mm, and 1000 mm). The findings highlighted the sensitivity of axial performance to angle curvature, section width and offset distance at column ends. The outcomes of this study provide valuable insights for the design and optimization of curved steel columns in structural engineering applications, particularly where stability and axial strength are critical
Augmentation method of fatigue data of welded structures based on physics-informed CTGAN
Full text linkVariable amplitude loading is frequently applied to welded structures in practical engineering, and fatigue failure is a prevalent problem. In recent years, machine learning is a useful technique for predicting fatigue life. However, it is challenging to acquire a sufficient number of reliable training samples for fatigue tests under variable amplitude loading. The machine learning models' accuracy and generalization capabilities are impacted by this. This work introduces a novel data augmentation approach utilizing physics-informed Generative Adversarial Networks (GAN). Data augmentation is accomplished by incorporating the traditional damage model - Ye model as constraints within the loss function of the Conditional Tabular GAN (CTGAN). The method combines physical laws of damage with CTGAN, which makes generated fatigue data conform to physical characteristics under two-step loading. Then the impact of generated data on model performance is evaluated on four machine learning models and compared to traditional damage models. The experimental results show that generated fatigue data helps machine learning models to get better prediction results compared with traditional models and unaugmented machine learning models, which significantly enhancing the precision of fatigue life predictions
Phase-field modeling for investigating the effect of rebar positioning and uniform versus non-uniform corrosion on concrete fracture
Full text linkRebar corrosion significantly affects the overall performance and the service life of reinforced concrete (RC) structures due to the reduction in the bond strength between concrete and rebar, leading to the delamination of the concrete cover. Numerous studies have been conducted using experimental, analytical, and simulation methods to explore corrosion-induced damage. Regarding simulation methods, previous studies have focused on either uniform or non-uniform corrosion, without an overall comparison between these two scenarios in terms of crack development and displacement of rust expansion. Furthermore, in brittle materials such as concrete, the strain tensor is split into a tension part and a compression part, in which only the strain energy of the tension part controls the crack development. Therefore, this paper provides some novel aspects: (i) Two parts of the strain tensor are orthogonal in the context of the inner product with the elastic stiffness tensor behaving as a metric. This orthogonal condition combined with the phase-field modeling, helps to improve the mechanical behaviors of the materials; (ii) The numerical method (i) is used to simulate and compare the crack path and displacement due to rust expansion of RC structures under uniform and non-uniform corrosion conditions. Several RC cross-sections are conducted as follows: (a) Cross-sections containing one or multiple asymmetrically arranged rebars, with the constant rebar area fraction and the concrete cover thickness unchanged; (b) Cross-sections containing four symmetrically arranged rebars with the 10mm rebar diameter (D10) and the concrete cover thicknesses changed; (c) Cross-sections containing four symmetric D10 rebars and the pores. Through several aforementioned numerical simulation examples, this paper provides an overview of uniform versus non-uniform corrosion-induced fracture in the typical RC cross-sections. This can guide the selection of the appropriate rebar positioning for the realistic RC structures, helping to mitigate rapid deterioration due to the rebar corrosion
Predictive modeling of PMMA-based polymer composites reinforced with hydroxyapatite: a machine learning and FEM approach
Full text linkThis research examines the mechanical characteristics of polymer composites (PMMA) that are reinforced with Hydroxyapatite (HAp), with a particular emphasis on the Elastic Modulus and Compressive Strength. The investigation employs a multifaceted approach that integrates experimental methods, micromechanical analysis, and machine learning techniques. Experimental assessments of Elastic Modulus and Compressive Strength were conducted at various HAp concentrations (5%, 15%, and 30%) and were compared with theoretical predictions derived from Representative Volume Element (RVE) and micromechanical frameworks, including Voigt and Reuss bounds. Various machine learning algorithms, such as Feedforward Neural Network (FFNN), Radial Basis Neural Network (RBNN), and Support Vector Machine (SVM), were used to predict the mechanical properties. The RBNN exhibited high accuracy (R² = 0.92; MAE = 0.05) for intermediate HAp levels (20-30%) but displayed instability at the extremes % of reinforcements values . The FFNN consistently provided lower estimates of the properties, whereas the SVM yielded robust and stable predictions that closely matched both experimental and theoretical results with the error of (2-5) % (Result value). This research highlights the effectiveness of integrating micromechanical modeling with machine learning to improve the prediction and comprehension of composite behavior, thereby offering valuable insights for the design and application of advanced materials
Damage mechanisms in hybrid composites: experimental characterisation and energy-based numerical analysis
Full text linkThis study analyses the failure mechanisms of bilayer hybrid composites, consisting of carbon and glass fibres embedded in an epoxy matrix, under bending loads. The objective is to evaluate how different hybrid configurations influence failure evolution and mechanical performance. To achieve this, specimens are submitted to 3-point bending tests, and 3D finite element models are developed to simulate the experimental setup. The numerical models incorporate a continuum damage mechanics model to capture intralaminar failure and a surface-based cohesive behaviour for interlaminar damage. The results show that hybrid laminates exhibit intermediate strength and displacement values compared to nonhybrid carbon and glass laminates, with the positioning of glass fibers significantly affecting bending force and displacement. Intralaminar damage is the primary failure mechanism in all configurations, followed by delamination. Additionally, placing glass fibers on the compression side reduces the overall damage, whereas placing them on the tensile side increases intralaminar failure before reaching the peak load. These findings contribute to optimizing the design of hybrid composites for bending applications by providing information about the relationship between material configuration and failure mechanisms, ultimately improving their structural efficiency and durability in engineering applications
Certain issues in the analytical integration of the Boussinesq problem
Full text linkThe Boussinesq solution, one of the fundamental problems in the theory of elasticity, enables an analysis of stresses and strains (displacements) in a semi-elastic space subject to surface loads. This solution has a form of formulas for displacements evoked by a concentrated force; these formulas can be treated as Green functions for calculation of displacements (and then – stresses) in a half-space loaded in any way at its surface z = 0. The study presents difficulties met during the analytical integration of the Green functions in the Mathematica environment as well as methods of coping with these difficulties. The authors are going to present particular issues which can be quite surprising and confusing, for example a failure to obtain a close result for definite integrals in Wolfram Mathematica or differences between results of calculations of the sum of integrals and the integral of the sums. The results of the study can help in establishing more exact benchmarks for the numerical methods applied in the analysis of settlement under foundations as well as other contact issues of the theory of elasticity based on the Boussinesq solution
Evaluation of thinning behaviour under the influence of plastic hardening and surface friction during small punch test
Full text linkUnderstanding the deformation response of the material involving thinning under small punch loading is vital to ensure structural integrity. This paper systematically investigates the effects of plastic hardening on the thinning process under different levels of surface friction between the punch, die and specimen. The small punch test conditions are modeled using Finite Element (FE) software of Abaqus. An axisymmetric model with a bi-linear constitutive material model incorporating different plastic hardening slopes is employed. Furthermore, the Coulomb’s friction coefficient between the disc-shaped specimen and the punch as well as the die varies between 0 (frictionless) to 0.7. The study found that the effect of plastic hardening on thinning process is negligible. On the other hand, the effect of thinning at the center of the specimen is significant under frictionless surface conditions. Thinning is observed to be dominant during the membrane stretching and plastic instability deformation stages. As the surface friction increases, the resistance to sliding deformation decreases. As a result, tensile instability is predicted at the location offset from the center of the specimen. Future efforts to model material behaviour and determine mechanical properties using small punch load conditions must consider the effects of friction
Investigation on the characterization and modelling of Fracture Process Zone behavior in Concrete Beams subjected to Three-Point Loading Tests
Full text linkThe fracture behavior of quasi-brittle materials such as concrete is characterized by the presence of a fracture process zone (FPZ) that precedes the main crack. Within this zone, various mechanisms, including the formation of microcracks, crack deflection, aggregate interlocking, and crack branching, contribute to the complex nature of the fracture behavior. Traditional experimental methods and other techniques often face challenges in fully capturing the micromechanical mechanisms occurring in the fracture process.To address this challenge, numerical models have been developed in the present study to investigate the evolution mechanisms of the FPZ. These models serve as valuable tools for simulating and analyzing the intricate behavior occurring at the microstructural level during the fracture process. By complementing experimental observations, these numerical approaches provide deeper insights into the fracture behavior of quasi-brittle materials and enhance the understanding of material failure. The outcome of present investigation clearly provides the evaluation method of FPZ in concrete beams of different sizes
Fatigue behaviour of high-strength low-alloy steel sheets: influence of loading direction and microstructure on microcrack initiation and growth
Full text linkThis work presents an in-depth study of the low-cycle fatigue behaviour of ferritic-pearlitic HSLA-420 high-strength steel sheets, with emphasis on the influence of loading direction on fatigue life and damage mechanisms. Plastic strain-controlled fatigue tests were conducted along the rolling (RD), transverse (TD), and diagonal (DD) directions. Despite the nearly isotropic tensile response associated with weak crystallographic texture and similar microstructural characteristics, fatigue life varied depending on the loading orientation. RD specimens showed the highest fatigue life, nearly doubling TD at low strain and remaining over 25% at high strain. DD behaved similarly to RD at low strain but approached TD at higher strain levels. The Coffin–Manson relationship was linear in RD, while TD and DD showed bilinear trends with a slope change at Δεp/2 = 1 × 10⁻³. Transmission electron microscopy revealed that dislocation structure evolution during cycling was direction-dependent. In RD, intragranular slip bands within ferrite grains dominated and acted as primary crack initiation sites. In contrast, TD and DD exhibited subgrain structures near grain boundaries, promoting strain localization and intergranular crack nucleation. At higher strain amplitudes, compact subgrains reinforced by cementite particles favored intergranular crack propagation in TD and DD samples, contributing to reduced fatigue life
Investigation on the tensile strength, hardness and wear properties in n-B4C reinforced Al7075 composites
Full text linkThe impact of n-B4C on the mechanical and tribological behavior of Al7075 particle reinforced composites were assessed by analyzing samples of the resultant nano-composites for micro-structure, hardness, tensile strength, and wear behavior using stircasting technology. According to microstructural research, nanoparticles were dispersed throughout the specimen space. Adding the wt. % of nano B4C resulted in a considerable improvement in hardness (17.89%) and tensile strength (13.75%). Because of the cleavage that forms on the fractured surfaces of Al+n-B4C nanocomposites, fractography analysis on the fractured tensile specimens revealed brittle fracture for the n-B4C reinforcement composites and ductile fracture for unreinforced aluminum. By adjusting the process conditions, the dry sliding wear characteristics of n-B4C reinforced aluminum alloys were investigated using Taguchi's Design of Experiment Methodology. The independent process factors were determined to be the applied load (7-21N), the sliding speed (750-1250 rpm), and the reinforcement composition (0-3 weight percent of n-B4C). The L27 orthogonal array by Taguchi was selected to react based on the coefficient of friction and wear rate. The following processing parameters were determined to be optimal for the highest wear rates: 750 rpm sliding speed, 7 N load, and 3 weight percent reinforcement. Similarly, the optimal processing parameters for assessing Coefficient of Friction (COF) were determined to be 3 wt. % reinforcement, 21 N load, and a sliding distance of 1250 rpm