Strength Model Research Articles

A Novel Inorganic Curing Foam for the Prevention of Spontaneous Coal Combustion and Its Characterization

ABSTRACT Aiming at the shortcomings of traditional inorganic curing foam materials, this paper carries out process optimization and ratio selection of inorganic curing foam through orthogonal test and single factor experimental. The performance of the improved inorganic curing foam was also tested by compression test, flexural test, and thermal insulation test. The experimental results show that the curing layer of inorganic curing foam can effectively insulate the heat generated by the spontaneous combustion of coal. The surface temperature of the inorganic curing foam under different heat sources was below 30 degrees. With the increase of hydration reaction time, the compressive strength of the material increased continuously, and the maximum compressive strength reached 3.36 Mpa at 28 days. In addition, the formula for calculating the porosity of inorganic curing foam materials was theoretically derived, and a prediction model for the compressive strength was established based on the porosity calculation formula.

Impact of Digital Marketing, Influencer, Live Features, and Reviews Shopee Purchase With Brand Image

The advancement of information and communication technology in the digital era has revolutionized various aspects of human life, including in the realm of business and marketing. Marketing activities that once relied on direct interaction between sellers and buyers have now shifted towards digital marketing that utilizes online platforms. Indonesia, as the largest economy in Southeast Asia, has made an e-commerce as the main driver of socioeconomic growth. Supported by a population of over 270 million people and ever-increasing internet penetration, the development of e-commerce in Indonesia has experienced rapid progress in recent years. This study aims to analyze the impact of digital marketing, influencer marketing, live features, and Online Customer Reviews on online purchase decisions on the Shopee platform with brand Image as an intervening variable. This research used a quantitative method with a data collection technique through an online questionnaire to 210 Shopee users in Indonesia. The measurement of construct validity and reliability was carried out using the measurement model (outer model). Then, the structural model (inner model) was used to test the model's strength, predictive ability, model significance, and the direct and indirect influence of independent variables on the dependent variable. The test tool employed SmartPLS 3.0. The results of this study indicate that the variables of Digital Marketing, Live Features, and Brand Image have a significant influence on Purchase Decisions on Shopee e-commerce. Furthermore, the variables of influencer marketing, live features, and Online Customer Review have a significant influence on Brand Image on Shopee e-commerce. Meanwhile, for the indirect influence, the variable live features have a significant influence on purchase decisions on Shopee e-commerce through Brand Image as an intervening variable.

Neural representations of beat and rhythm in motor and association regions.

Humans perceive a pulse, or beat, underlying musical rhythm. Beat strength correlates with activity in the basal ganglia and supplementary motor area, suggesting these regions support beat perception. However, the basal ganglia and supplementary motor area are part of a general rhythm and timing network (regardless of the beat) and may also represent basic rhythmic features (e.g. tempo, number of onsets). To characterize the encoding of beat-related and other basic rhythmic features, we used representational similarity analysis. During functional magnetic resonance imaging, participants heard 12 rhythms-4 strong-beat, 4 weak-beat, and 4 nonbeat. Multi-voxel activity patterns for each rhythm were tested to determine which brain areas were beat-sensitive: those in which activity patterns showed greater dissimilarities between rhythms of different beat strength than between rhythms of similar beat strength. Indeed, putamen and supplementary motor area activity patterns were significantly dissimilar for strong-beat and nonbeat conditions. Next, we tested whether basic rhythmic features or models of beat strength (counterevidence scores) predicted activity patterns. We found again that activity pattern dissimilarity in supplementary motor area and putamen correlated with beat strength models, not basic features. Beat strength models also correlated with activity pattern dissimilarities in the inferior frontal gyrus and inferior parietal lobe, though these regions encoded beat and rhythm simultaneously and were not driven by beat alone.

Axial compression stress-strain relationship of lithium slag rubber concrete

Replacing cement with lithium slag and fine aggregate with rubber in concrete solves waste disposal, reduces material consumption, boosts sustainability, and enhances concrete performance. A set of prismatic concrete specimens with varying proportions were designed and experimentally tested in order to study the compressive stress-strain behavior of lithium slag rubber concrete (LSRC). The main factors affecting the specimens were lithium slag substitution ratio (SL=0%, 10%, 20%, 30%) and rubber substitution ratio (SR=0%, 5%, 10%, 15%). The results demonstrated that the LSRC exhibited good integrity during the damage. Furthermore, the incorporation of lithium slag (LS) was found to effectively compensate for the reduction in compressive strength due to the incorporation of rubber. When 10% of the fine aggregate was replaced with rubber and 20% of the cement was substituted with lithium slag, the axial compressive strength, elastic modulus, and peak strain of the tested specimens increased by 21.57%, 6.92%, and 17.26%, respectively. Compared with ordinary concrete, LSRC has good toughness, impact resistance and durability with minimal loss of strength, and has broad application prospects in engineering fields (such as airports, highways, housing expansion joints, concrete floors and railway concrete sleepers, etc.). Based on the experimental data, simplified modified equations to predict the compressive strength, elastic modulus, peak strain and axial stress-strain constitutive model of LSRC were proposed, so as to promote the development of LSRC.

Pore collapse, shear bands, and hotspots using atomistics-consistent continuum models for RDX (1,3,5-trinitro-1,3,5-triazinane): Comparison with molecular dynamics calculations

Shock-induced energy localization is a crucial mechanism for determining shock sensitivity of energetic materials (EMs). Hotspots, i.e., localized areas of elevated temperature, arise when shocks interact with defects (cracks, pores, and interfaces) in the EM microstructure. The ignition and growth of hotspots in a shocked energetic material contribute to rapid chemical reactions that can couple with the passing shock wave, potentially leading to a self-sustained detonation wave. Predictive models for shock-to-detonation transition must correctly capture hotspot dynamics, which demands high-fidelity material models for meso-scale calculations. In this work, we deploy atomistics-guided material models for the energetic crystal RDX (1,3,5-trinitro-1,3,5-triazinane) and perform tandem continuum and all-atom molecular dynamics (MD) simulations. The computational setup for the continuum and MD simulations are nearly identical. The material models used for the calculations are derived from MD data, particularly the equations of state, rate-dependent Johnson–Cook strength model, and pressure-dependent shear modulus and melting temperature. We show that a modified Johnson–Cook model that accounts for shear-induced localization at the pore surface is necessary to represent well—relative to MD as the ground truth—the inelastic response of the crystal under a range of shock conditions. A head-to-head comparison of continuum and atomistic calculations across several metrics of pore collapse and energy deposition demonstrates that the continuum calculations are in good overall agreement with MD. Therefore, this work provides improved RDX material models to perform physically accurate meso-scale simulations, to enhance understanding of hot spot formation, and to use meso-scale hot spot data to inform macro-scale shock simulations.

Optimizing the effect of heat treatment on the mechanical properties (tensile Strength and hardness) of Hyphaene Thebaica nut; A machine learning and Taguchi approach

The demand for innovative and sustainable biomaterials in healthcare and biotechnology has fueled the need for advancements in its engineering performance. However, the limited properties of biomaterials without the integration of synthetic reinforcements or supports pose a challenge. Consequently, there is a growing necessity for the development of full cycle eco-friendly materials. This study focuses on enhancing the mechanical properties of Doum palm nuts through heat treatment for engineering applications. The research employs the Taguchi optimization technique and genetic algorithms to determine the optimal combination of heating temperature and holding time, aiming to achieve the highest tensile strength and hardness. The results showcase the effectiveness of the proposed approach, revealing the optimal tensile strength of 7.32 MPa at a treatment temperature of 50 °C and a holding time of 120 min. Similarly, the optimum hardness of 60.3 Hv is attained at a heat treatment temperature of 100 °C and a holding time of 120 min. The study further investigates chemical changes through Fourier transform infrared analysis and optical micrographs. Regression models for tensile strength and hardness are developed, and the global optimum is found using a genetic algorithm solver. The findings of this study demonstrate significant and remarkable improvements in the mechanical properties of biomaterials, highlighting their potential for demanding engineering applications. The optimized heat treatment parameters provide a path towards enhanced biomaterials performance and offer promising avenues for the development of tougher and more resilient materials. This research contributes to the field of biomaterials engineering, paving the way for the design and production of eco-friendly materials with superior mechanical properties, thus addressing the ongoing demand for innovative and sustainable solutions in various industries.

Investigation of the failure mechanisms of photocurable resins under explosive shock loads

Resin components produced by photocuring technology and 3D printing are commonly utilized as seals across various media owing to their flexible molding, precise structure, and excellent watertightness. Under certain specific conditions, it is necessary to detach the photocurable resin seals from the main body using explosive cord blasting. However, the mechanical performance of photocurable resins under explosive shock loads remains underexplored. This study aimed to explore the mechanisms underlying the destruction of photocurable resins by explosive cords in different environments. We conducted explosive shock experiments on photocurable resin specimens both in air and underwater and employed explicit dynamics software to simulate the damage process of the resin material. The experiments showed that a 10 mm thick specimen in air reached a critical fracture state under the equivalent explosive shock of the explosive cord, leading to adjustments in the constitutive parameters of the photocurable resin model in the simulations. In addition, the JH-2 strength and failure model was applied in AUTODYN explicit dynamics software to characterize the mechanical properties of the resin materials. The fluid–structure interaction method was utilized for modeling and simulating the shock process on photocurable specimens in air and water, clarifying the role of groove shape in the fracture state of the specimens and the failure mechanisms of the photocurable resin materials under explosive shock.

Stress-strain behaviour of pre-damaged concrete confined with natural fibre reinforced polymer

In recent years, using environmentally friendly construction materials has become a new trend in structural engineering. This research presents the application of natural fibre reinforced polymer (NFRP) composites for repairing damaged structural concrete members. The compressive behaviour of pre-peak damaged concrete confined with two types of NFRP composites was investigated. The tensile properties of jute fibre reinforced polymer (JFRP) and sisal fibre reinforced polymer (SFRP) were examined and compared with those of carbon fibre reinforced polymer (CFRP) using coupon tests. Subsequently, a series of pre-damaged concrete cylinders, confined with JFRP, SFRP, and CFRP, were subjected to uniaxial compression tests. The results showed that JFRP and SFRP are effective in providing confinement to pre-peak damaged concrete. The strength and ductility of pre-peak damaged concrete confined with NFRP increased with the number of NFRP layers. The cost-effectiveness of FRP-confined concrete was also evaluated, with JFRP being the most economically efficient. The eco-strength efficiency (ESE), defined as the increased compressive strength per unit of CO2 emissions, shows that JFRP is the most effective. This highlights JFRP's potential for sustainable construction. Finally, the compressive strength models were proposed. The models demonstrate improved accuracy in predicting the peak compressive strength of pre-peak damaged concrete confined with NFRP compared to existing models.

Prediction model for hardness and tensile strength of graphene reinforced AZ 61 alloy based composite using Metaheuristic Algorithm

It is possible to produce improved material performance through the utilization of computational intelligence approaches such as genetic algorithms optimization technique which are discussed in this paper. The optimization of processes and the development of models that are driven by data are the key uses of these technologies. This article offers a comprehensive introduction of the topic of materials and discusses the ways in which computational intelligence techniques might be utilized to develop new materials. The present study envisages the development of data driven model that enables to derive desirable properties of the said composite; so, in order to secure the optimized subset of requirements (process parameters), a metaheuristic optimization tool is employed. We invoke the use of the Genetic Algorithm (GA) optimizing tool in association with linear regression, so as to achieve the best combination of hardness and tensile strength of AZ61 graphene nanoplate (GNP) composite.

Bond behavior between geopolymer recycled brick aggregate concrete and steel bars after exposure to high temperatures

The use of recycled brick aggregate (RBA) as the replacement for natural coarse aggregate in geopolymer concrete is an effective method for waste recycling. When the formed geopolymer recycled brick aggregate concrete (GRBC) is employed in structures, it may encounter the risk of fire. Whether the steel bars and GRBC can work together after a fire is the basis for structural safety and analysis. However, previous research on the bond behavior between GRBC and steel bars after exposure to high temperatures is still lacking. In this paper, pull-out tests on 50 GRBC and 50 ordinary recycled brick aggregate concrete (ORBC) were conducted to investigate the effects of temperature (20 °C, 200 °C, 400 °C, 600 °C, and 800 °C), and RBA replacement ratio (0 %, 25 %, 50 %, 75 %, and 100 %). The test results show that the failure pattern of GRBC specimens changes from splitting-pull-out failure to pull-out failure as the heating temperature increases. The bond strength decreases with the increase of temperature or RBA replacement ratio. When the temperature rises from 200 °C to 800 °C, the bond strength of GRBC pull-out specimens decreases by 11.54 %–72.33 % compared with the specimens at room temperature, and the decrease rate is lower than that of ORBC. The bond strength of GRBC specimens with a 100 % replacement ratio decreases by 46.34 %–53.74 % compared with those without replacement. The peak slip of GRBC specimens generally increases with increasing temperature or decreasing RBA replacement ratio. Based on the post-temperature test results and the room temperature model, suitable models for the bond strength and peak slip of GRBC after high temperatures were established. The predicted results of bond stress−relative slip curves were in good agreement with the test results. The findings of this study can provide a foundation for the application of GRBC, and provide a basis for its structural analysis after high temperatures.

Bond Strength and Development Length Model for Corroded Reinforcing Bars

Bond Strength and Development Length Model for Corroded Reinforcing Bars

Implementing Sobol's Global Sensitivity Analysis to SFRC's Flexural Strength Predictive Equation

Steel fibers are essential for SFRC since they strengthen the material’s resistance to bending and cracking stresses and guarantee its endurance. However, the uncertainty of input parameters such as concrete compositions and steel fibers causes the stochasticity of flexural strength. The study uses big data to analyze the influence of fibers and other concrete compositions on the flexural strength properties of SFRC. For this purpose, the study focuses on developing predictive models for SFRC flexural properties based on a comprehensive database comprising two hundred and seven experimental results recorded by seventeen researchers. Bayesian Model Averaging is employed to identify significant components that influence the overall flexural strength and to develop a predictive flexural strength model. Monte Carlo simulation generates big data by utilizing the probability distribution of input variables and the predictive flexural strength model. The study used Sobol’s global sensitivity analysis method to assess various input parameters’ sensitivity to SFRC flexural strength based on the generated database. The impact order of input variables on the flexural strength is identified, as determined by the Sobol’ Indice.

Artificial neural network models for predicting breaking strength and abrasion resistance properties of woven fabrics with different chenille yarns

This study was carried out with aim of predicting some performance properties of fabrics with changing chenille yarn parameters. In this study, different chenille yarns were produced with parameters of pile length, yarn count and yarn type. Three different yarn types were used: polyester, acrylic and viscose. Four different yarn counts were used for each yarn type and four different pile lengths were used for each yarn count. Thus, 48 woven fabrics were obtained from 48 different yarns. The estimated properties included breaking strength in weft-warp direction and abrasion resistance, and these properties formed output data. As input data, yarn properties such as pile length, yarn count and yarn type; fabric properties such as fabric density, fabric thickness and fabric weight were used. Neural network toolbox in MATLAB was used to develop Artificial Neural Network (ANN) models. Different network structures were used to estimate three performance features, thus aiming to obtain more accurate results. Additionally, predictions were made with linear and nonlinear multiple regression models, and compared with ANN models. The R2 values ​​obtained from ANN models for breaking strength in the warp-weft direction and abrasion resistance were found to be 0.95, 0.74, 0.87, respectively, while they were found to be 0.32, 0.40, 0.41 for linear multiple regression models and 0.65, 0.27, 0.60 for nonlinear multiple regression models. The obtained ANN models were successful by a clear margin compared to statistical models.

Predicting and optimizing maximum flexural strength of planar composite plate shear walls – concrete filled with the application of LR and RSM

Traditional theoretical models, while conservative, overlook the complex, nonlinear interactions between material and geometric parameters that influence wall flexural strength. Additionally, the extent to which these models maintain their conservative assumptions across varying parameters remains uncertain. This study aims to develop advanced predictive models for the flexural strength of planar composite plate shear walls—concrete filled (C-PSW/CF) by employing Linear Regression (LR) and Response Surface Methodology (RSM). Central Composite Design (CCD) was utilized to design numerical experiments to analyze the effects of varying parameters, including steel plate yield strength, concrete compressive strength, wall depth, depth-to-thickness ratio, and reinforcement ratio. A validated complex finite element modeling (FEM) procedure was used to analyze numerical experiments involving varying parameters of the selected variables. Both LR and RSM were applied to the generated dataset to formulate predictive equations for the maximum moment capacity of walls. Interaction plots were developed to reveal the nonlinear relationships between the variables and identify the most effective parameters influencing the wall strength. Comparisons between theoretical predictions, RSM, and LR models revealed that RSM provided the most accurate predictions, closely aligning with FEM results and effectively capturing the nonlinear interactions between wall parameters. The findings of this paper contribute to the development of more practical, accurate and optimized design methods for C-PSW/CF, ultimately improving both structural safety and efficiency.

Damage of thin target plates impacted by conical projectiles with varying apex angles in ballistic application: experimental, FEA and ANN integrated approach

In the current study, an experimental setup consisting of smoothbore 30-caliber powder gun was employed to launch spherical projectiles (3 g/ϕ9) in the ballistic range of velocities from 500 to 1700 m/s and obliquity (0, 33 and 65°), impacting 2 mm thin Steel 1006 target plates. Crater dimensions (major and minor dia.) obtained from series of FE simulations run for the above configuaration was validated by corresponding experimental data. Subsequently, a fullscale 3D FE model considering 8-noded hexahedral elements was used to discretize SS304 conical projectiles (replacing spherical projectile) with various apex angles viz. 40°, 60°, 80° and 100° impacting on single (3 mm) and double layer (1.5 mm each) steel 1006 targets (replacing 2 mm target) in LS dyna. The material behavior was characterized using J–C strength and damage models, along with Gruneisen Equation of State. An erosion algorithm was used in the explicit FE code LS-DYNA to remove undesirable elements. In the next stage, Adam and Nadam optimizers have been employed in ANN models developed using Python code within the Tensor-Flow framework. The Keras Tuner library in the Tensor-Flow framework was used for hyper parameter tuning. The ANN models trained using simulation data successfully predicted the residual KE of conical projectiles with intermediate apex angles of 90°, 70°, and 50° across twelve different impact scenarios. The models with Adam and Nadam optimizers achieved mean squared error (MSE)/coefficient of determination (R2)/mean absolute percentage error (MAPE) values of 109.44/0.998/6.72 and 91.19/0.998/7.76, respectively.

Interfacial interaction mechanism and theoretical bond strength model between UHPC-CA and steel bar

The bond property is the basis of the bear collectively load between the steel bar and Ultra-High Performance Concrete with Coarse Aggregate (UHPC-CA). In this paper, the theoretical calculation method of bond strength between UHPC-CA and high-strength steel bar is studied. The chemical adhesion strength, friction coefficient, and mechanical interlock process of the two were investigated respectively through the refined bond property tests. The failure model is established from the interlock failure characteristics, and the bond strength is obtained theoretically. Results show that the chemical adhesion strength and friction coefficient between UHPC-CA and steel bars are 0.07 MPa and 0.5, respectively. The friction coefficient between UHPC-CA is 1.15. The interlock failure process of UHPC-CA and steel bars can be divided into three stages: uncracked stage, crack development stage, and split stage. The crack develops at an angle of 52° to the steel bar's axis and tends to stagnate at a height of 4.5 times the rib. A theoretical bond strength model is established and it is proved that the model is applicable not only to UHPC-CA but also to UHPC. This study can provide a theoretical basis for determining the anchorage length of steel bars in the design of UHPC-CA/UHPC structure, and can also provide a reference for the formulation of relevant standards.

Comparative Studies on In-Plane Shear Behavior of Masonry Wallettes Retrofitted with Mortar, UHPC, and ECC Ferrocement: Shotcrete and Prefabricated Panels

Masonry walls, prevalent in many historical and contemporary structures, often require retrofitting to meet modern safety and performance standards. Motivated by the need for efficient and reliable retrofitting solutions, this research investigates the potential of various types of ferrocement, reinforced with high-strength steel meshes, to enhance the in-plane shear behavior of brick masonry walls. Through rigorous diagonal compression tests on nine masonry wallettes, the study evaluated the performance of ferrocement overlays in terms of overlay configuration (asymmetric or symmetric), cementitious material type (mortar, ECC, or UHPC), the presence or absence of steel mesh, and construction method (shotcrete or prefabricated panels). The results indicated that UHPC and ECC overlays significantly enhanced post-cracking deformation capacity. Compared to the asymmetric retrofit, the symmetric retrofit led to more uniform deformation in the retrofitted wallette, effectively improving the maximum shear by up to 119 %. While UHPC shotcrete was more effective in improving the shear strength, ECC shotcrete led to the highest rupture shear strain and pseudo-ductility for the retrofitted wallette, reaching 0.83 % and 17.2, respectively. Moreover, although steel mesh inclusion in the ferrocement overlay was crucial in enhancing post-peak ductility, its impact on shear strength of the retrofitted wallettes was minimal. The test results also demonstrated that the developed prefabricated UHPC panels emerged as an effective retrofitting solution to simultaneously improve the strength, deformation capacity, and ductility for masonry wallettes. In addition to the experimental investigation, a new strength model, accounting for various key design variables, was developed, offering reliable shear strength predictions for the retrofitted wallettes.

Demonstrating scalability between two blender types using DEM

Powder blending is a critical step in pharmaceutical manufacturing that can impact product quality such as tablet tensile strength. This study utilized the Discrete Element Method (DEM) to investigate blending in a 5-liter mini-batch and a 2-liter Turbula blender. DEM parameters were calibrated using small-scale powder characterization tests, so that the particle behavior in the DEM simulations matches the measured behavior. The research explored the effects of blender designs and process conditions on blending and lubricant dispersion. A predictive model for tablet tensile strength was developed. The model takes the lubricant’s dispersion via the lubrication energy into account. The model is then used to predict the tablet tensile strength depending on the chosen process parameters, blending speed, duration, and fill level. DEM simulations enabled scaling between the two blenders, providing valuable insights for a semi-continuous manufacturing process based on mini-batch blending. The findings contribute to a deeper understanding of blending mechanics, offering potential enhancements in pharmaceutical manufacturing efficiency and product consistency.

Mechanics guided data-driven model for seismic shear strength of exterior beam-column joints

This study presents an enhanced predictive model for the seismic shear strength of exterior beam-column joints (BCJs). Initially, the principles of strut-and-tie mechanism and variable selection procedures were first utilized to identify the most influential parameters. Subsequently, an evolutionary algorithm, specifically multigene genetic programming (MGGP), was utilized to search for the near-optimal predictive model. The dataset used to develop, train, and test the proposed model was compiled from previously published tests, focusing specifically on cyclically loaded exterior BCJs that encountered shear and flexure -shear failures. The prediction performance of the developed model was assessed through various statistical measures, and then compared with that of other existing models. Additionally, sensitivity analyses were also performed to identify the influence and importance of each design parameter. The results demonstrated that the methodology employed in this study yielded an elegant model that adheres to the underlying mechanics and provides higher prediction accuracy compared to existing models. Furthermore, the sensitivity analyses showed that BCJ shear strength positively correlates with concrete compressive strength, beam reinforcement, joint transverse reinforcement, column intermediate vertical reinforcement, and axial load ratio, while it negatively correlates with the joint aspect ratio. Among these design parameters, beam reinforcement has the greatest influence on the model response, followed by concrete compressive strength. Conversely, column intermediate vertical reinforcement and axial load ratio have the least impact on the model response. The notable prediction capabilities and robustness demonstrated by the developed model render it an efficient design tool with promising potentials for adoption by practicing engineers and for consideration in design guidelines.

Predict the modelling of cement concrete strength using Taguchi and ANOVA method

Predict the modelling of cement concrete strength using Taguchi and ANOVA method