Strength Model Research Articles

Enhanced Predictive Ensemble Learning Models for Compressive Strength of Concrete Modified with Bentonite Clay

This study addresses the logistical challenges of testing bentonite plastic concrete in the field by developing predictive models for compressive strength (CS) using Python-based machine learning techniques. The models, including random forest, adaptive boosting, extreme gradient boosting, and gradient boosting regression tree, were enhanced with forensic-based investigation optimization. A dataset of 285 CS records was used to train and validate these models. SHAP analysis highlighted the impact of various inputs like gravel, bentonite, curing time, and cement on CS. Results show that the GBRT-FBIO model outperformed the others in CS prediction, indicating its superior effectiveness for material science applications.

Bayesian-Optimized Deep Learning for Predicting Compressive Strength of Sustainable Eggshell Foamed Concrete

<p><span lang="EN-US">The use of eggshell-based foamed concrete represents a sustainable approach to enhancing environmental friendliness in construction materials. This study investigates the predictive modelling of the compressive strength of eggshell-based foamed concrete through a deep learning model, fine-tuned via Bayesian optimization. Utilizing a dataset of 360 samples with diverse input parameters, the model was optimized with four hidden layers (28, 21, 28, and 21 neurons) and the Rectified Linear Unit (ReLU) activation function. The model demonstrated excellent predictive accuracy, achieving a mean squared error of 0.0522, a mean absolute error of 0.0382, and an R² value of 0.9548 over 200 epochs. Notably, the water/cement ratio emerged as the most influential factor in prediction accuracy. This research provides a robust, AI-driven method for predicting the compressive strength of sustainable construction materials, contributing to advancements in environmental technology and the optimization of eco-friendly construction practices.</span></p>

Parametric and nonparametric estimation stress strength model based on copula function under first-failure progressively unified hybrid censoring schemes

Parametric and nonparametric estimation stress strength model based on copula function under first-failure progressively unified hybrid censoring schemes

Bonding strength of short carbon fiber reinforced PA6 composites ultrasonic welding joints

AbstractShort carbon fiber composites have high specific modulus, high specific strength and excellent formability, which are suitable for short‐term and environmentally friendly ultrasonic welding. In this study, ultrasonic welding experiments were conducted on short carbon fiber reinforced PA6 composites using a servo‐driven non‐metallic ultrasonic welder. The weld specimens were analyzed for spot weld morphologies, interfacial bonding strength, failure modes and compressive shear fracture to establish the correlation between weld energy and weld quality. The results indicate that when the welding energy is 450 J, the short carbon fibers are interlaced at the interface, which has a pinning effect, thereby enhancing the joint strength reaching 2990 N, and the welding efficiency reaches 46%. Besides, the compressive shear section is divided into regions. The proportion of heat and area in different compressive shear regions are quantitatively analyzed, and the calculation model of joint strength is proposed. Meanwhile, the correlation between welding energy and its correlation, as well as three different shear failure modes, is obtained. This work is helpful for the optimization of carbon fiber reinforced thermoplastic (CFRTP) ultrasonic welding process parameters and the evaluation of joint strength.Highlights A special fixture for ultrasonic welding was designed to optimize the welding energy process of ultrasonic welding CFRTP, and the properties of ultrasonic welded joints were analyzed. When the welding energy is 450 J, a high shear strength welded joint of 2990 N was successfully prepared. The shear fracture of the welded joint was successfully obtained by designing a special clamp for compression shearing. The welding area was qualitatively divided according to the temperature measurement results of the interface center. In addition, the heat and area ratio of different zones in the compression‐shearing area under different welding energy were quantitatively analyzed, and the calculation model of joint strength was proposed. At the same time, the welding energy and its correlation, as well as three different shear failure modes were obtained. The short fiber distribution characterization and pores effect on the joint's strength were analyzed. The appearance of porosity defects has a significant weakening effect on the welded joint.

Numerical Simulation and Experimental Validation of the Acoustical Target Strength of Bluefin Tuna Swimbladders Derived from 3D Computed Tomographic Images

The swimbladder, when present, is the main contributor to the acoustical target strength (TS) of fish. Numerical modeling of target strength must include swimbladder dimensions, orientation, and shape for the proper estimation of target strength and its directivity. Several Atlantic Bluefin tuna (Thunnus thynnus, ABFT) specimens between 90 and 100 cm of fork length were studied by performing computed tomographic (CT) post-mortems in both fresh and frozen states. ABFT swimbladder 3D models were derived for the first time to be compared with experimental TS measurements through numerical simulation methods, using the Method of Fundamental Solutions (MFS). The numerical estimation (−23.3 dB) agreed with the experimental measurement of TS (−22.1 dB) performed in a tank with tuna with a mean fork length of 100 cm, showing the importance of considering realistic swimbladder shapes and swimming behavior in the numerical simulation of TS.

Hybrid soft computing-based predictive models for shear strength of exterior reinforced concrete beam-column joints

Hybrid soft computing-based predictive models for shear strength of exterior reinforced concrete beam-column joints

Understanding romantic relationships using strength and strain model in the context of COVID-19.

Understanding romantic relationships using strength and strain model in the context of COVID-19.

Intrusion Detection: A Comparison Study of Machine Learning Models Using Unbalanced Dataset

The worldwide process of converting most activities of both corporate and non-corporate entities into digital formats is now firmly established. Machine learning models are necessary to serve as a tool for preventing illegal intrusion onto different networks. The machine learning (ML) model's strengths and drawbacks pertain to intrusion detection (IDS) tasks. This study used an experimental methodology to assess the efficacy of various ML models, including linear SVC, LR, random forest (RF), decision tree (DT), and XGBoost, in detecting intrusion on the UNSW NB15 datasets. The objective is to compare the strengths and shortcomings of these models. Data exploration, Feature engineering, selection and a test set of 15%, a validation set of 15%, and a training set of 70% respectively were used for data splitting. Performance evaluation was carried out using accuracy, recall, precision F1-score and confusion matrix plotted. The outcome of the experiment shows a percentage of 92.71% (1, normal) and 7.29% (0, attack) for normal traffic and attack traffic respectively. Performance evaluation results showed that RF and XGBoost outperformed the other ML models. Hence, ML models can effectively be used to detect system attacks. We intend to expand this research in the future and use the paradigm in a real-world setting with further conclusions and justifications.

Early age bond-slip behavior of reinforcing steel bars in concrete subjected to uniaxial lateral pressure: An experimental study

The bond between reinforcing bars and concrete is a crucial factor concerning the serviceability of reinforced concrete structures, particularly for those during construction in some developing areas where plain bars were still used as reinforcements. Despite numerous studies on early age bond behavior between steel bars and concrete, little research has been conducted to quantify the effect of uniaxial lateral pressures on this bond. This study involved testing a total of 378 pullout specimens to evaluate the early age bond-slip behavior of plain bars in concrete under uniaxial lateral pressure. The main test parameters included the concrete ages (1, 3, 7, 14 and 28 days), lateral pressures (0, 0.1, 0.2, 0.4 and 0.6 times the compressive strengths of concrete), concrete strength grades (C20, C35 and C45), and bar diameters (12, 16 and 20 mm). Based on the experimental results, a series of analytical expressions were developed for bond strength, maximum bond slip and residual bond stress predictions considering the effects of early age and lateral pressure. The results demonstrate that degradation of bond strength at early ages can be effectively mitigated by uniaxial lateral pressures. As the age decreases from 28 to 1 days, the bond strength exhibits a significant decrease of up to 51.59 %, while this adverse effect can be reduced by around 10 % in the presence of lateral pressures. Increasing the pressure level from 0 to 0.4 results in a significant increased early age bond strength by 112.6 % to 199.0 % for different bar diameters. However, the residual-to-maximum bond stress ratio remains nearly constant at 0.49 regardless of age and lateral pressure, as the pressure primarily contributes to frictional forces at the bar-concrete interface. An age-dependent bond-slip constitutive model considering the effect of uniaxial lateral pressures was finally proposed and verified with the test results. The paper concludes that given the concrete compressive strength at 28 days, the proposed bond strength and bond stress-slip models can predict the early age bond responses of plain bars in concrete under uniaxial lateral pressures with reasonable accuracy.

CFD-FEM Analysis for Functionality Prediction of Multi-Gear Pumps

A comprehensive model for evaluating the functionality of a multi-gear pump has been developed. The integrated model for assessing the functionality of a multi-gear pump contains a computational fluid dynamics analysis (CFD) model combined with a finite element method (FEM)-based strength model. Two submodels were linked: a CFD submodel to evaluate the internal pressure distribution of the pump and a structural FEM submodel to calculate the stresses and structural displacements of the pump due to fluid pressure. Finite element analysis in SolidWorks 2016 was used to evaluate the strength of the gear joints of the pump gears. As the pressure of the working fluid increases from 6 to 20 MPa, a linear increase in Mises stresses is observed. At the shaft, these stresses increase to 226.2 MPa, and at the tooth mouths, they reach a maximum value of 205.5 MPa. With the increase in torque on the drive shaft from 100 to 500 N·m, there is a significant increase in Mises stresses localized in the contact zones of the shaft with the drive gear. Analysis of the data obtained showed that the displacements caused by the pressure of the working fluid are insignificant compared to the displacements arising under the action of torque. With increasing pressure and torque, there is a tendency to decrease the safety factor, which indicates a decrease in the safety factor of the design of the multi-gear pump. The safety factor is not provided at a torque of 400–500 N·m. The simulation results are confirmed by correlation analysis. The average approximation error is 5–7%.

Machine learning driven design of high-performance Al alloys

Aluminum (Al) alloys with both high strength and thermal conductivity (TC) are promising structural materials for wide application across different industries. Yet, design of such alloys is challenging, since strength and TC often share a trade-off. In this paper, we build prediction models for TC and ultimate tensile strength (UTS) of Al alloys using eXtreme gradient boosting (XGBoost) and support vector machine (SVM) algorithms, respectively. The models take physical descriptors from the alloy composition into account. Lasso and Gini Impurity algorithms were adopted for feature engineering. Guided by the models, an Al-2.64Si-0.43Mg-0.10Zn-0.03Cu alloy with TC over 190 W·m-1·K-1 and UTS over 220 MPa was designed. The alloy was fabricated and tested by experiment, and its UTS and TC are close to the model prediction. Microstructure characterization suggests that the fragmented and spherical Si phase, along with a few non-spherical Si phases, may be a key reason for the improved properties.

Construction of a Wilms tumor risk model based on machine learning and identification of cuproptosis-related clusters

BackgroundCuproptosis, a recently identified type of programmed cell death triggered by copper, has mechanisms in Wilms tumor (WT) that are not yet fully understood. This research focuses on examining the link between WT and Cuproptosis-related genes (CRGs), with the goal of developing a predictive model for WT.MethodsFour gene expression datasets related to WT were sourced from the GEO database. Subsequently, expression profiles of CRGs were extracted for differential analysis and immune infiltration studies. Utilizing 105 WT samples, clusters related to Cuproptosis were identified. This involved analyzing associated immune cell infiltration and conducting functional enrichment analysis. Disease-characteristic genes were pinpointed using weighted gene co-expression network analysis. Finally, the WT risk prediction model was constructed by four machine learning methods: random forest, support vector machine (SVM), generalized linear and extreme gradient strength model. The best-performing machine learning model was chosen, and a nomogram was created. The effectiveness of this predictive model was validated using methods such as the calibration curve, decision curve analysis, and by appiying it to the TARGET-GTEx dataset.ResultsThirteen differentially expressed Cuproptosis-related genes were identified. The infiltration level of CD8 + T cells in WT children was lower than that in Normal tissue (NT) children, and the level of M0 infiltration of macrophages and T follicular helper cells was higher than that in NT children. In addition, two clusters of cuproptosis-related WT were identified. Enrichment analysis results indicated that genes in cluster 2 were primarily involved in cell division, nuclear division regulation, DNA biosynthesis process, ubiquitin-mediated proteolysis. The SVM model was judged to be the optimal model using 5 genes. Its accuracy was confirmed through a calibration curve and decision curve analysis, demonstrating satisfactory performance on the TARGET-GTEx validation dataset. Additional analysis revealed that these five genes exhibited high expression in both the TARGET-GTEx validation dataset and sequencing data.ConclusionThis research established a link between WT and Cuproptosis. It developed a predictive model for assessing the risk of WT and pinpointed five key genes associated with the disease.

Preliminary investigation of the effects of high-temperature thermal activation on the activity of graphite tailings and the mechanical properties of graphite tailings mortar

The disposal of graphite tailings, a byproduct of graphite mining, poses significant recycling challenges. This study explores eco-friendly cement mortar by partially replacing sand with activated graphite tailings, focusing on waste utilization. Activating the pozzolanic properties of graphite tailings at various thermal activation temperatures converts them into high-quality binder materials. Quantitative assessments of graphite tailings' activity, conducted through ion leaching tests and raw material diffraction analysis, produce an activity index Ka, evaluating their performance under different thermal activation temperatures. Variable testing of substitution rates and activation temperatures examines changes in mechanical properties, microstructure, and pore distribution of the graphite tailings mortar. Optimum results reveal a 30 % substitution rate and a 750°C activation temperature. Graphite tailings, as pozzolanic binder materials, enhance mortar strength by improving particle size distribution, reducing "micro-area bleeding" and "boundary effects". After thermal activation, reactive minerals on the tailings' surface undergo secondary hydration reactions with cementitious products, modifying chemical structures and micro-morphology at the interface. Semi-quantitative XRD analysis and FT-IR spectroscopy characterize the ensuing chemical reactions, unveiling molecular bond and functional group changes. The superior performance of thermally activated graphite tailings primarily stems from increased active SiO2 and Al2O3 post-activation. The active SiO2 and Al2O3 reacts with cementitious products to form C-(A)-S-H gels, filling micro-pores at the tailings-mortar interface, which is the key characteristic that enables graphite tailings to serve as high-quality pozzolanic binder aggregates. A predictive model for compressive strength, utilizing reference models from solid waste mortar concrete, quantifies the positive impact of chemical activation. The theoretical results obtained from this predictive model closely align with the experimental findings.

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.

Leveraging Data Science Technology for Advancing Credit Risk Assessment

The evaluation of credit risk (CR) has become prominent in recent years, particularly among banks, as default rates are on the rise and economic insecurity remains persistent. Traditional credit scoring techniques oftentimes are inadequate and provide little means for risk estimation, necessitating the development of new models using data science methodologies. In this study, a novel Intelligent Dwarf Mongoose tuned Light Gradient Boosting Machine (IDM-LGBM) model that boosts the accuracy of CR and improves forecasting performance, is introduced. The Light Gradient Boosting Machine model's hyperparameters were optimized using the Intelligent Dwarf Mongoose technique, improving the model's predictive strength. The CR dataset was gathered from the Kaggle platform. The data is then pre-processed using Z-score normalization. To evaluate the efficiency of the suggested IDM-LGBM technique, which has been implemented employing a Python platform. Results show that the IDM-LGBM model performed significantly better than conventional methods in terms of recall (98.1%), accuracy (97.2%), F1-score (97.4%), and precision (96.5%). Subsequent studies could concentrate on addressing real-time data streams and enhancing models to respond to the changing credit environment.

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 0.07MPa 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.

Investigation on precipitate behavior and mechanical properties of Al-Zn-Mg-Cu alloys with various Zn/Mg ratios

The effect of Zn/Mg ratio on the microstructure and properties of Al-Zn-Mg-Cu alloys under single-step and double-step aging processes were investigated by using scanning electron microscopy (SEM), transmission electron microscopy (TEM), electron backscatter diffraction (EBSD), as well as hardness, room temperature tensile, and fracture toughness testing. During single-step aging, as the Zn/Mg ratio increases, the aging response of the alloys accelerates. However, the peak hardness and strength gradually decreased while the average size of precipitates increased. During double-step aging, as the secondary aging time increases, the strength and hardness of the alloys gradually decrease. Additionally, the higher the Zn/Mg ratio, the faster the strength and hardness decline. Under the T74 temper, the alloy with higher Zn/Mg ratios exhibits lower hardness and strength but better fracture toughness and larger precipitate sizes. A simple method was proposed to statistically determine the critical diameter size of GPII zone and η′ phase, which was approximately 4.47 nm. A strength model was developed to predict the yield strength of alloys, considering factors such as solid solution strengthening, grain boundary strengthening, and precipitation strengthening. The model demonstrates good predictive accuracy for both peak-aged and T74 temper alloys, providing valuable guidance for alloy design and microstructure regulation.

Discrete Joint Random Variables in Fréchet-Weibull Distribution: A Comprehensive Mathematical Framework with Simulations, Goodness-of-Fit Analysis, and Informed Decision-Making

This paper introduces a novel four-parameter discrete bivariate distribution, termed the bivariate discretized Fréchet–Weibull distribution (BDFWD), with marginals derived from the discretized Fréchet–Weibull distribution. Several statistical and reliability properties are thoroughly examined, including the joint cumulative distribution function, joint probability mass function, joint survival function, bivariate hazard rate function, and bivariate reversed hazard rate function, all presented in straightforward forms. Additionally, properties such as moments and their related concepts, the stress–strength model, total positivity of order 2, positive quadrant dependence, and the median are examined. The BDFWD is capable of modeling asymmetric dispersion data across various forms of hazard rate shapes and kurtosis. Following the introduction of the mathematical and statistical frameworks of the BDFWD, the maximum likelihood estimation approach is employed to estimate the model parameters. A simulation study is also conducted to investigate the behavior of the generated estimators. To demonstrate the capability and flexibility of the BDFWD, three distinct datasets are analyzed from various fields, including football score records, recurrence times to infection for kidney dialysis patients, and student marks from two internal examination statistical papers. The study confirms that the BDFWD outperforms competitive distributions in terms of efficiency across various discrete data applications.

Microscopic simulation on triaxial compression creep of rockfill based on subcritical crack propagation theory

Over time, natural cracks within particles propagate under tensile stress, leading to the delayed breakage of these particles, which significantly contributes to the time-dependent deformation of rockfill materials. In this study, a delayed strength model for spherical particles with virtual cracks is proposed using subcritical crack propagation theory and validated through indoor single-particle creep tests. Based on the model, particles are represented as ideal spheres in the context of triaxial compression creep simulations for rockfill, with special attention to particle size effects, discreteness, and temporal factors. The combined influence of long-term strength and maximum contact force is crucial in determining the delayed particle breakage. Comparative analysis with indoor triaxial creep tests demonstrates that the Discrete Element Method (DEM) simulations accurately model the creep deformation phenomena related to delayed particle breakage in rockfill. Furthermore, statistical analysis indicates that a minor fraction of delayed particle breakage has a negligible impact on the temporal distribution of the Normalized Maximum Contact Force (NMCF).

INFLUENCE AND PREDICTION OF SINTERED AGGREGATE SIZE DISTRIBUTION ON THE PERFORMANCE OF LIGHTWEIGHT ALKALI-ACTIVATED CONCRETE

The most effective method for mitigating the adverse environmental impacts of traditional concrete involves substituting cement and natural aggregate with waste and byproduct resources. Utilizing sintered lightweight aggregate (fly ash) in geopolymer concrete emerges as an efficient solution for managing and disposing of significant amounts of fly ash. The influence of sintered aggregate size distribution on the performance of alkali-activated concrete, focusing on compressive strength improvement. The study employs sintered fly ash aggregate (SFA) as coarse aggregate, aiming to optimize packing density through proper particle distribution. The highest compressive strength is achieved with a mix featuring 75% 4-8mm and 25% 8-12mm SFA. Regression-based strength models are developed, exhibiting good alignment with conventional concrete models. Thin section techniques reveal enhanced aggregate-matrix interaction due to the porous structure of SFA. The study emphasizes the potential of SFA in geopolymer concrete for sustainable construction. Lightweight geopolymer concrete, owing to its lower density, significantly reduces the overall structural load.