Material Property Parameters Research Articles

Landslide Analysis with Incomplete Data: A Framework for Critical Parameter Estimation

Landslides are one of the most common geohazards, posing significant risks to infrastructure, recreation, and human life. Slope stability analyses rely on detailed data, accurate materials testing, and careful model parameter selection. These factors are not always readily available, and estimations must be made, introducing uncertainty and error to the final slope stability analysis results. The most critical slope stability parameters that are often missing or incompletely constrained include slope topography, depth to water table, depth to failure plane, and material property parameters. Though estimation of these values is common practice, there is limited guidance or best practice instruction for this important step in the analysis. Guidance is provided for the estimation of: original and/or post-failure slope topography via traditional methods as well as the use of open-source digital elevation models, water table depth across variable hydrologic settings, and the iterative estimation of depth to failure plane and slope material properties. Workflows are proposed for the systematic estimation of critical parameters based primarily on slide type and scale. The efficacy of the proposed estimation techniques, uncertainty quantification, and final parameter estimation protocol for data-sparse landslide analysis is demonstrated via application at a landslide in Colorado, USA.

Design and modeling of a programmable morphing structure with variable stiffness capability

The development of structures capable of both dynamic shape morphing and stiffness modulation has significant potential in various applications. However, such structures often suffer from bulkiness and control complexity. This paper addresses these challenges by exploring a scaled structure that integrates morphing capabilities and variable stiffness within a compact configuration. For the first time, we establish a comprehensive set of design criteria and obtain the previously unexplored design space, focusing on geometric parameters including layer thickness, target shape radius, the number of scales, and the number of periods per scale. Through extensive finite element simulations, we evaluate the impact of material property and geometric parameters on the performance of the scaled structure, emphasizing the role of coefficient of friction. Our findings identify a critical threshold for the coefficient of friction above which morphing ability is hindered. Additionally, we uncover a trade-off between morphing capability and stiffness variation ability, which we overcome by modifying the surface structure of the scales. The optimal design is found to be a superellipse shape with an exponent of ∼1.9. The practical potential of this structure is demonstrated through three applications: a soft gripper, a phone stand, and a foldable box, showcasing its versatility in real-world scenarios. This research provides a foundational approach for designing morphing scaled structures, offering valuable insights into optimizing morphing capability and stiffness variation ability for broader engineering applications.

Enhancing Manufacturing Processing Stability and Efficiency with Linear-Regression Analysis: Modeling on a Flow-Drill Screw (FDS) Joining Process

The instability (in processing time) in the flow-drill screwing process is undesired but inescapable due to variations in material property, gauge, and process parameters. A substantial number of materials and lab labor need to be used to test and control the variability of the real manufacturing joining process. To enhance the stability and efficiency of the screwing process, this study seeks multi-disciplinary collaboration by applying linear-regression modeling. Six hundred and forty-eight data points were collected and split into an 80% training set for model building and a 20% test set for model validation. A multiple linear-regression model was built. The results indicated that, compared to variable base level (6000 rpm rotational speed and 1100 N downforce), higher rotational speed (8000 rpm, 7000 rpm), greater downforce (1200 N, 1300 N), and their interaction were significantly associated with passage (processing) time, while the switch point did not significantly affect passage time. The interaction plot and effect size were adopted to provide measurements of the effect magnitude on processing time. The coefficient of determination indicated that 86% of the variability in the passage time can be explained by this model. Statistical analysis, such as data visualization, statistical modeling, and other data-driven analysis methods, can be used to detect underlying relationships between variables, investigate variations, and make predictions in the manufacturing process. The outcomes from the data-driven analysis can benefit from improving the economical manufacturing system, refining the processing setting, and reducing test material costs, labor, and lead time.

Regression-classification ensemble machine learning model for loading capacity and bucking mode prediction of cold-formed steel built-up I-section columns

This paper presents a regression-classification ensemble machine learning (ML) model for loading capacity and, in particular, buckling mode prediction with respect to cold-formed steel (CFS) I-section columns. The ML model comprises two sub-models, including the regression sub-model for load capacity prediction and the classification sub-model for buckling mode prediction. A total of 541 experimental and numerical data for CFS built-up I-section columns with varies geometric dimensions, material properties and buckling modes were collected from the literature to construct a dataset. To improve the accuracy and the explainability of the ML model on relative small-scale dataset, physical information-enhanced features based on the knowledge of the direct strength method were adopted as the input features of the ML model, including the yield strength, the elastic local buckling load, the elastic distortional buckling load and the elastic global buckling load, instead of directly using the parameters of geometric dimensions and material properties as the input features. The appropriateness of seven classical machine learning algorithms was compared, namely Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Adaptive Boosting (ADA), Extreme Gradient Boosting (XG), and Categorical Boosting (CB). The statistical analysis showed that the XG algorithm had the highest level of accuracy for both loading capacity and buckling mode prediction. In addition, the explainability analysis indicated that the developed ML model successfully learned the mechanical characteristic of CFS built-up I-section columns. Finally, the developed ML model was further compared with the existing codified method in the literature. The comparison results indicated that the developed ML model can significantly improve the level of accuracy for loading capacity and buckling mode prediction, which provides a promising alternative choice for the design of CFS built-up I-section columns

Nonlinear dynamic response of a sandwich plate with negative Poisson’s ratio honeycomb-core layer under low-velocity collision impact

In this paper, a nonlinear analytical model is presented for the low-velocity collision impact of a sandwich plate with auxetic honeycomb-core layer. The auxetic feature of the honeycomb material is realized by mathematically expressing the effective material coefficients in terms of material property and cellular geometric parameters through a homogenization method. The higher order shear deformation theory, the von Kármán nonlinearity theory, and a modified Hertz contact law which accounts for the contact pressure distribution and indentation effect, are employed to establish the kinematic relations. The Newmark time integration scheme in conjunction with the direct iterative method is utilized to establish a solution procedure for the nonlinear dynamic governing equation. The verification of the presented model with the data in published literatures is carried out, followed by a series of numerical analyses for influences of cellular geometric features and impactor’s initial conditions (such as impactor’s initial velocity, mass, and nose curvature radius) on the nonlinear dynamic response. The results of numerical analyses show that the geometric features of the unit cell in the honeycomb-core and impactor’s initial conditions can cause significant influences on the nonlinear collision impact behaviors of the system.

Inverse design of TPMS piezoelectric metamaterial based on deep learning

Piezoelectric metamaterials are a class of composite structural materials with a piezoelectric effect that achieve efficient coupling between electrical and mechanical energy through their unique microscopic design. Typically, the tailoring of cellular architectures paves the way for unlocking the potential to achieve unprecedentedly designed materials with specific properties. We have developed three different inverse design frameworks for metamaterials, using deep learning (DL) techniques and underpinned by Bayesian optimisation. These frameworks reveal precise one-to-many correlations between material properties and geometric parameters within the triple periodic minimal surface (TPMS) piezoelectric metamaterial. For the first time, we have introduced the Progressive Latin Hypercube Sampling (PLHS) method to the inverse design process of metamaterials, highlighting the effectiveness of uniform sampling to improve the efficiency of deep learning models. The conditional variational autoencoder (cVAE), the conditional generative adversarial network (cGAN) and the multilayer perceptron (MLP) are studied for comparative analysis. All three models incorporate an inverse design network and a feature predictor to construct a continuous latent representation of a large number of hybrid TPMS (H-TPMS). After extensive training, the models can generate various piezoelectric porous structures that accurately meet the required material property parameters. Our results demonstrate the high accuracy of cVAE in inverse design, with 98.92% agreement for piezoelectric values and 96.44% agreement for mechanical properties. The three inverse design frameworks show different generalisation capabilities, with cVAE performing best. This study thoroughly analyses the strengths and limitations of each model, providing valuable insights for researchers seeking inverse design recommendations.

Fractal theory-based contact analysis of surface microtextured involute spline coupling

Accurate estimation of the contact stress of the aviation involute spline coupling is the basis of accurate estimation of its wear. In view of this, in this work, a fractal contact coefficient for involute spline coupling with surface texture based on the gear fractal contact model and M-B fractal contact model was established. Then, the relationship between fractal dimension and actual contact area, as well as the relationship between actual contact area and load were derived. And also, a fractal contact model for involute spline coupling with surface texture was established. Finally, the influence of geometric parameter on the contact coefficient and fractal contact model was analyzed. The results indicate that: the fractal dimension (D), roughness amplitude parameter (G), and material property parameter (ϕ) have important effects on the fractal contact model of involute spline coupling on the tooth surface; There must be an appropriate fractal dimension (D) value that makes the contact state optimal; As the roughness amplitude parameter (G) value increases, the surface contact condition deteriorates and the surface contact strength decreases. Conversely, as the material property parameter (ϕ) value increases, the surface contact condition improves and the surface contact strength increases. It is a preliminary exploration for accurately calculating the contact stress of involute spline coupling, laying a solid theoretical foundation for accurately predicting the wear of involute spline coupling with surface texture.

Nonlinear transient response of rotating graphene platelets reinforced metal foams blades with initial geometric imperfection

In this paper, a new mechanical model including initial geometric imperfection is developed to investigate the nonlinear transient response of graphene platelets reinforced metal foams (GPLRMF) blades subjected to blast load. Combined with higher-order shear deformation plate theory (HSDT) and von-Karman geometric nonlinearity, the nonlinear motion equations are established, in which the Coriolis force, centrifugal force as well as initial geometric imperfection are simulated. Considering cantilever boundary condition, the partial differential equations are transformed into a series of ordinary differential equations by Galerkin discretization. Using the four order Runge-Kutta algorithm, the nonlinear transient response is numerically researched, and the time histories and phase trajectories are obtained. In addition, the effectiveness of the current research is validated by comparing the results with the existing valuable literatures. The influences of rotating speed, presetting angle, initial geometric imperfection, material property, damping coefficient, and blast load parameter on the dynamic deflection and instantaneous speed are presented. Numerical results reveal that the nonlinear transient dynamics of the composite blades under blast load are significantly influenced by initial geometric imperfection.

High Temperature Experiments and Properties of Carbon Polyimide Composites

The interlayer shear tests of AC741/M55JB carbon fiber reinforced composite at room temperature, 400℃, 600℃ and 800℃ were carried out.The longitudinal and transverse shear tests of AC741/CCF800 carbon fiber reinforced composite were carried out at room temperature, 400℃, 600℃ and 800℃ dry conditions, as well as the experiments to calculate material property parameters. The results showed that the main failure mode of the sample in the interlayer shear test was stratified damage, and the fracture of the upper surface material was not obvious.This is due to the different types of tests, the interlayer shear pattern is short and small, and it is not easy to occur the fracture damage of the material without stratification.In addition, with the increase of temperature, the interlaminar shear strength also decreased significantly.In crossbar shear test, the maximum in-plane shear stress, maximum in-plane shear strength and in-plane shear modulus of the material gradually decrease with the increase of temperature, thus changing the bearing property of the material.

Electro-mechanical contact behavior of rough surfaces in extreme temperature

Revealing the electro-mechanical contact characteristics between rough surfaces at extreme temperatures is critical to accurately assessing the service performance of equipment in aerospace, energy and other fields. In this paper, a theoretical model of electro-mechanical contact behavior at extreme temperatures is developed based on the consideration of the effect of temperature on material property parameters and interfacial morphologies. It reveals the mechanism of temperature variation on the contact behavior of rough surfaces. The results show large temperature changes affect the contact resistance and the real contact area, because of the in significantly different contact behavior between high temperature and low temperature. This study helps us to gain a deeper understanding of the effects of extreme temperatures on the contact behavior and provides a reference for the design of interfacial conductivity in engineering structures.

Nonlinear dynamic analysis of a geometrically imperfect sandwich beam with functionally graded material facets and auxetic honeycomb core in thermal environment

In this study, a model for the nonlinear dynamic analysis of the geometrically imperfect sandwich beam with functionally graded material (FGM) facets and auxetic honeycomb core is proposed. The auxetic feature of the honeycomb material is realized by a homogenization method, which gives the expressions of the effective material coefficients in terms of material property and cellular geometric parameters. The nonlinear motion equation of the geometrically imperfect beam is derived based on the Reddy's higher shear deformation theory, von Kármán nonlinear theory, as well as general geometric imperfection function. The thermal effect on the material properties and structural rigidity is also taken into account in this model. The Newmark method and Newton–Raphson iterative method are employed to solve the nonlinear motion equation of the sandwich beam. In numerical examples, the dynamic response of the sandwich beam under two types of load, i.e. moving load and impulse load, are discussed in details. The influences of various factors (such as material parameter, geometric imperfection, and thermal condition) on the nonlinear dynamic behaviors of the sandwich beam are elaborately investigated. The numerical results show those factors can play significant roles in the bearing ability, structural stiffness, or nonlinear dynamic behaviors of the sandwich beam in certain conditions. Some key conclusions on the influences are drawn from the numerical results, which can be a useful reference for future investigations.

A reliability-base method for thermal cracking prediction in asphalt concrete

This study focuses on understanding the impact of environmental factors that contribute to the deterioration and damage of Hot Mix Asphalt (HMA) pavements. One significant problem faced by HMA pavements is transverse (thermal) cracking, which refers to the formation of cracks perpendicular to the centerline of the pavement. Over the past few decades, researchers have put significant effort into analyzing and understanding how thermal cracking affects HMA pavements. Mechanistic-empirical models play a crucial role in this process, but they require a substantial amount of input data related to the pavement's structural and material properties. Obtaining this data can be done through various methods with different levels of quality, which affect the reliability of the pavement performance responses. The probabilistic base designs and specifications can consider the variation of input data and the impact of material and structural together with environmental parameters on pavement deterioration. In this project, transverse cracking, as one of the major asphalt concrete pavement cracking, particularly in the freezing areas, will be studied, and a reliability-based method will be introduced. To reach this goal, the long-term pavement performance (LTPP) database was used. A regression model from the key parameters of pavement material and structural properties and environmental and traffic indicators was generated. Then through the structural reliability method, the probability of overpassing of transverse cracking amount from the threshold for a pavement design was assessed.

Experimental-based statistical models for the tensile characterization of synthetic fiber ropes: a machine learning approach

This study investigated the tensile behavior of some prevalent synthetic fiber ropes made of polyester, polypropylene, and nylon polymeric fibers. The aim was to generate well-documented experimental statistics and develop simplified stress–strain constitutive laws that can describe the ropes' tensile response. The methodology involved analyzing the thermal history of the fibers using the DSC technique, tensile testing of fibers and yarn components of the rope, and conducting 196 rope tensile tests with optimum testing conditions. Based on the test results, an experimental database of the ropes' tensile characteristics was established, containing different parameters of material properties, rope construction, fiber processing, fiber tensile properties, and rope tensile responses. Subsequently, ANN models were developed and optimized using MATLAB based on the generated dataset's inputs and outputs to predict the studied ropes' tri-linear stress–strain profiles. The results showed that the ANN models accurately predicted the stress–strain properties of ropes represented by the tri-linear approximation with an error of about 5% for the failure strength and strain. The study provides insight into the process-structure–property relationship of synthetic fiber ropes and contributes to minimizing the cost and effort in designing and predicting their tensile properties while contributing to the practical industry.

Prediction of shear strength in UHPC beams using machine learning-based models and SHAP interpretation

To provide more accurate and reliable predictions of the shear strength of ultrahigh-performance concrete (UHPC) beams, in this study, the machine learning (ML) approaches were employed to develop the data-driven models, and the ML models were interpreted using the Shapley additive explanations (SHAP) method. It was found that the ensemble models, particularly CatBoost, outperform individual ML models and traditional empirical models. The geometric dimensions and shear span-to-depth ratio were the most influential features for predicting the shear strength of UHPC beams, followed by the parameters of reinforcement and material properties of the UHPC.

Full-range stress–strain relationship and fracture model for laser cladding additively manufactured 316L stainless steel sheets

Laser cladding (LC) is being used increasingly in civil engineering owing to its high efficiency and minimal distortion since requiring little heat input; especially for repairing or strengthening damaged metal structures. LC stainless steel sheets are characterised by a rounded stress–strain response, with two “knee” regions and no sharply defined yield point. Such behaviour cannot be accurately represented by using previous stress–strain models. Moreover, determining the true stress–strain curve and toughness coefficient for the fracture model is essential to carry out a non-linear finite element analysis for solving elastoplastic problems. In the present study, one simplified full-range engineering stress–strain model for LC 316L stainless steel sheets is proposed based on the three-stage Hradil model and is validated against the experimental results of thirty tensile coupon tests. The proposed model redefines the second and third stages of the stress–strain curve following the experimental observations. All parameters in this simplified model can be derived directly from only four basic material property parameters: the initial elastic modulus, 0.2 % proof stress, ultimate stress and the strain corresponding to the ultimate stress. For defining the fracture model of LC stainless steel sheets, an average weight method is employed to determine the post-necking true stress–strain curves. The recommended values of the weight factor and toughness coefficient for the LC sheets are also obtained based on the available tensile coupon test results. The comparisons between the experimental force–displacement curves of the tensile coupons and the finite element analysis results using the proposed stress–strain relationship and fracture model for the LC sheets shows both results to be in good agreement. Therefore, the stress–strain relationship and fracture model proposed in this paper is suitable for use in the analytical and numerical modelling of structures having LC 316L stainless steel sheets.

Prediction of Isotropic Rough Surface Directional Spectral Emissivity with Surface-Morphology-Dependent Modelling

The surface directional spectral emissivity of rough metal surfaces in industry is of concern in infrared temperature measurement. In this research, the height and slope possibility density functions are introduced as variables, and the directional spectral emissivity of isotropic rough surface is modelled accordingly. The model is designed to derive the directional spectral emissivity of rough metal surfaces from the surface morphology (possibility distribution of the height and slope) and the material property parameters (refractivity). Then, a sandblasted surface is taken as a case study. The sandblasted surface morphology is measured. A Polynomial surface is proposed to describe the sandblasted surface morphology and is compared with a Gaussian surface and a Cox–Munk surface. Finally, the directional spectral emissivity measurement and infrared temperature measurement are conducted. It is shown that the predicted directional spectral emissivity and measured temperature with the surface-morphology-dependent isotropic rough surface directional spectral emissivity model have high precision. In this work, the possibility distribution of the height and slope of the surface is introduced as independent variables to provide better accuracy compared to the reported models. In some cases, the error of the infrared temperature measurement could be reduced to 20% (80 degrees, compared to Gaussian surface). This work contributes to improving the accuracy of IR temperature measurement of rough surfaces.

Analysis of a cracked neo-Hookean substrate with initial stress under a rigid punch

The coupling problem of a cracked neo-Hookean substrate with initial stress under a rigid punch is examined in the current work. Fourier series is used to transform the mixed boundary value problem(MBVP) into singular integral equations (SIEs). The obtained SIEs is solved by using Gauss-Chebyshev collocation method. The stress distributions on the contact zone and crack surface, and related singularities are present. The numerical experiments examine how the punch position, punch width, crack length and propagation parameter affect the stress field. The result showed that: for k = 1 and unchanged material property parameters, the incremental stress distribution on the initially stressed neo-Hookean base is very similar to the Cauchy stress distribution of the isotropic linear elastic base.

Determination of Material and Interaction Properties of Granular Fertilizer Particles Using DEM Simulation and Bench Testing

The discrete element method (DEM) is an effective tool for obtaining qualitative and quantitative information on particle motion, which aids in the design and optimization of agricultural equipment structures. The accuracy of the DEM simulation parameters significantly impacts the simulation results. This study employed a combination of high-speed camera measurement, DEM simulation, and validation tests to determine the material and interaction property parameters for fertilizer particles. The basic parameters (triaxial size, bulk density, density, and coefficient of static friction) and coefficients of restitution between fertilizer and material were measured for three fertilizer varieties. There was a significant difference in the angle of repose between various material plates and fertilizer particles. The calibration values of coefficients of restitution and coefficients of rolling friction between fertilizer particles were optimized using the Box–Behnken method. The angle of repose was significantly affected by the coefficient of static friction and the coefficient of rolling friction between the fertilizer particles. The determined values for the coefficient of restitution, coefficient of static friction, and coefficient of rolling friction between the fertilizer particles were 0.323, 0.381, and 0.173, respectively. The error in the angle of the repose test was less than 3.0%, and the variation coefficient for each row consistency was less than 1.68 percentage points under the optimal simulation parameters. DEM simulations of the angle of repose and each row consistency variation coefficient test using the measured parameters can accurately predict the experimental results. The findings of this paper provide a theoretical basis for the DEM study of fertilizer particles.

Precision Grinding Technology of Silicon Carbide (SiC) Ceramics by Longitudinal Torsional Ultrasonic Vibrations.

Silicon carbide (SiC) ceramic material has become the most promising third-generation semiconductor material for its excellent mechanical properties at room temperature and high temperature. However, SiC ceramic machining has serious tool wear, low machining efficiency, poor machining quality and other disadvantages due to its high hardness and high wear resistance, which limits the promotion and application of such materials. In this paper, comparison experiments of longitudinal torsional ultrasonic vibration grinding (LTUVG) and common grinding (CG) of SiC ceramics were conducted, and the longitudinal torsional ultrasonic vibration grinding SiC ceramics cutting force model was developed. In addition, the effects of ultrasonic machining parameters on cutting forces, machining quality and subsurface cracking were investigated, and the main factors and optimal parameters affecting the cutting force improvement rate were obtained by orthogonal tests. The results showed that the maximum improvement of cutting force, surface roughness and subsurface crack fracture depth by longitudinal torsional ultrasonic vibrations were 82.59%, 22.78% and 30.75%, respectively. A longitudinal torsional ultrasonic vibrations cutting force prediction model containing the parameters of tool, material properties and ultrasound was established by the removal characteristics of SiC ceramic material, ultrasonic grinding principle and brittle fracture theory. And the predicted results were in good agreement with the experimental results, and the maximum error was less than 15%. The optimum process parameters for cutting force reduction were a spindle speed of 22,000 rpm, a feed rate of 600 mm/min and a depth of cut of 0.011 mm.

Elastoplastic Analysis of Shear Behavior of Multicell Shaped Concrete-Filled Steel Tubular Connection Based on Unified Theory

Connection between multicell shaped concrete-filled steel tube (MCFST) columns and steel beams gives full play of strength and properties of steel tube and core concrete and has many advantages in mechanical and seismic performance. Shear behavior of the connection is crucial to damage performance of special-shaped CFST structure under seismic load. However, few studies focused on shear behavior of the MCFST connection. This study investigates elastoplastic shear behavior of the MCFST connection. Nine specimens were tested under constant axial load and lateral low cyclic loading. The test results showed that as height to thickness ratio of column section increased, the plastic status of the stresses of the flange limb steel webs was obviously different from those of the web limb steel webs, and initial shear stiffness of the panel zone increased. In order to give analytical methods for shear behavior of the MCFST connection under compressive and shear force at the material level to engineers, according to unified theory the analytical model of MCFST composite material in the panel zone is equivalent to that of concrete-filled circular steel tube of web and flange limbs considering the difference of the stresses of the steel webs and interaction between the web and flange limbs. Based on the previous research and analysis of test results, migration and evolution are carried out from analytical models for shear behavior of the MCFST connection at the material level to that at the member level. The analytical expression of initial shear stiffness of the MCFST connection is derived, and the theoretical results correlate well with the experimental results. In addition, the analytical expression of shear stiffness of the MCFST connection in plastic hardening stage is obtained. The calculation formulas are proposed to predict the shear stiffness of the MCFST connection in the elastic and plastic hardening stages by geometrical parameters, material property parameters, and stress state of the steel webs.