Search Results (51)

Accurate prediction of the behavior of alloys and metal matrix composites during high-temperature deformation requires strict consideration of the loading history. To address this problem, a hybrid rheological model for flow stress prediction has been developed, combining a phenomenological description of the yield stress with a recurrent neural network based on the NARX (Nonlinear AutoRegressive with eXogenous inputs) architecture. The memory effect is formed by expanding the input parameters with the response values from the previous step. The identification of the weight coefficients of the NARX neural network is implemented by training an equivalent multilayer perceptron. To improve the generalization ability of the model and eliminate its dependence on a fixed discretization step, the training dataset includes data obtained under non-monotonic changes in the strain rate over time and a variable time interval. The article justifies the structure of the model input parameters, excluding the accumulated strain from the input set due to its lack of informativeness during active softening processes. Verification of the hybrid model on the 7075/2.5% TiC composite in the temperature range of 300–500 °C demonstrated an average relative error of 1.5% when predicting modes that were not involved in the training. The predicted flow stress values fall within the experimental scatter interval of ±5% and accurately reproduce the local features of the flow stress curves. The proposed model and its identification technique provide correct consideration of the deformation history under the complex interaction of hardening and softening processes. Full article

This paper is the empirical validation companion to our prior methodology paper introducing the Automated Zero Trust Risk Management with DevSecOps Integration (AZTRM-D) methodology, conducted through multi-vector adversarial testing on physical NVIDIA Jetson Orin Nano hardware. AZTRM-D unifies DevSecOps automation, the NIST Risk Management Framework, and Zero Trust architecture with AI orchestration via Cybectr Sentinel, featuring six AI subsystems with formal specifications. Testing spanned three progressive hardening stages across seven attack categories under a blind three-tester protocol with inter-rater agreement analysis. Factory-default devices were fully compromised in under five minutes. After full hardening, zero successful breaches were recorded across any tested vector. The CI/CD pipeline achieved a vulnerability detection rate of 96.8% (Wilson 95% CI: [0.891, 0.991]). Sentinel delivered 94.1% precision, 91.8% recall, and 4.2 min average detection time within 12–18% CPU overhead on edge hardware. A 14-capability comparative analysis against five established frameworks found seven capabilities unique to AZTRM-D. The 93.7% adversarial detection rate is reported against DiCE-generated counterfactual inputs and is bounded by the black-box threat model used in evaluation; gradient-based white-box attack evaluation is documented as a scoped Stage 4 future-work item. All three testers are affiliated with Cybectr LLC, the developer of AZTRM-D and Cybectr Sentinel; this conflict of interest is the most significant limitation of the present work, and independent third-party laboratory validation is the highest-priority Stage 4 deliverable. Full article

The hole expansion ratio (HER) is a critical formability metric for advanced high-strength steels (AHSS) in automotive applications; however, its experimental determination is costly and time-consuming. This study presents a machine learning framework for HER prediction using physics-informed synthetic data generation to address data scarcity challenges. A dataset of 300 AHSS conditions was generated based on validated empirical relationships from the literature, incorporating chemical composition, microstructure fractions, and mechanical properties. Multiple machine learning algorithms were evaluated, with the optimized Gradient Boosting model achieving excellent predictive performance on an independent test set (R² = 0.80, RMSE = 5.81%, MAE = 4.93%). The feature importance analysis revealed physically meaningful rankings, with the ultimate tensile strength dominating (40.9%), followed by the bainite volume fraction (15.1%), martensite volume fraction (14.7%), and strain hardening exponent (12.4%). These rankings align with the established metallurgical understanding, thereby validating our synthetic data approach. The results demonstrate that machine learning models trained on physics-informed synthetic data can accurately predict the HER values with errors comparable to the experimental variability, providing a practical tool for accelerated AHSS design and optimization in automotive applications. Full article

Scalability of the original test design for the box assembly with removable component (BARC) structure is of interest in the field of experimental structural analysis. As complex structures become increasingly difficult to test experimentally the larger they become, it is a common test practice to use a scaled-down representative model to understand the characteristics of these systems. For complex structures with non-rigid boundary conditions, there exists a gap in understanding the effects of scalability and welding. To gain a better understanding of the outcomes of this phenomenon, the dynamical effects of upscaling the dimensions of the BARC structure are analyzed. Three variations of the BARC are investigated experimentally and computationally, namely, the original BARC system, the BARC system upscaled at 1.5 times the size of the original model, and the BARC system upscaled at two times the size of the original model. The original BARC is tested to investigate the properties of the predetermined boundary conditions. Because the upscaled BARC systems are manufactured using welding, an investigation of the variability of results due to welding imperfections is conducted to evaluate its effects on the vibrational properties of the systems. The dominant resonant frequencies of the three systems are identified through an impact hammer test. The results are then compared to those obtained through finite element analysis, in which both datasets show agreement. In general, as the BARC system is upscaled, the resonant frequencies decrease without inducing mode switching for the selected boundary conditions, indicating that the larger systems are less rigid. To understand the trends of nonlinear softening/hardening and nonlinear damping, forced vibration experiments conducted in the form of true random and controlled stepped-sine excitations are performed. The results show that, in general, as the BARC system is upscaled, changes in the nonlinear properties of the system are induced. With regard to the effects of using welding to manufacture BARC systems, the results prove that variations in welding can lead to non-negligible variations in the vibratory responses of the BARC system. Additionally, several types of harmonic vibrational testing are investigated to understand the physics behind their varied responses. Overall, this work shows that upscaling the BARC system can be beneficial to researchers who require a less rigid system for investigations and that manufacturing of BARC systems by welding can be a cost-effective alternative to subtractive manufacturing. Full article

Fibrin clots with strain-hardening characteristics exhibit pronounced material nonlinearity and acoustic dispersion under ultrasound, leading to waveform distortion and shock formation during finite-amplitude wave propagation. However, peak-shock stress is limited by viscoelastic dissipation and dispersion, constraining the efficiency of ultrasound in applications such as thrombus ablation. To overcome this limitation, a shock wave amplification method using designed multi-wave-packet sequences is proposed. Based on a power-law model from quasi-static compression tests, shock generation under a single sinusoidal pulse was first simulated. The dual-wave-packet chasing strategy was then developed, in which the amplitude, frequency, and time delay of the second packet were tuned to achieve effective superposition with the precursor. The waveform superposition factor (WSF) was introduced for quantitative evaluation. Numerical results demonstrate that this strategy can significantly increase the peak-shock-wave stress, with a maximum gain of 22.7%. Parametric analysis further identified amplitude as the dominant factor influencing wavefront steepness and amplification effectiveness. This study provides a novel method and theoretical support for developing efficient and controllable ultrasonic sequences for thrombolysis. Full article

Optimizing heat treatment processes requires an understanding of the complex relationships between compositions, processing parameters, microstructures, and properties. Traditional experimental approaches are costly and time-consuming, whereas machine learning methods suffer from critical data scarcity. In this study, gradient boosting models were developed to predict microstructural phase fractions and mechanical properties using synthetic training data generated from an established metallurgical theory. A 400-sample dataset spanning eight AISI steel grades was created based on Koistinen–Marburger martensite kinetics, the Grossmann hardenability theory, and empirical property correlations from ASM handbooks. Following systematic hyperparameter optimization via 5-fold cross-validation, gradient boosting achieved R² = 0.955 for hardness (RMSE = 2.38 HRC), R² = 0.949 for tensile strength (RMSE = 87.6 MPa), and R² = 0.936 for yield strength, outperforming the Random Forest, Support Vector Regression, and Neural Networks by 7–13%. Feature importance analysis identified the tempering temperature (38.4%), carbon equivalent (15.4%), and carbon content (13.0%) as the dominant factors. Model predictions demonstrated physical consistency with the literature data (mean error of 1.8%) and satisfied the fundamental metallurgical relationships. This methodology provides a scalable and cost-effective approach for heat treatment optimization by reducing experimental requirements based on learning curve analysis while maintaining prediction accuracy within the measurement uncertainty. Full article

The pervasive deployment of large language models (LLMs) has given rise to mounting concerns regarding the safety and security of the content generated by these models. Nevertheless, the absence of comprehensive evaluation methods constitutes a substantial obstacle to the effective assessment and enhancement of the safety and security of LLMs. In this paper, we develop the Safety and Security (S&S) Benchmark, integrating multi-source data to ensure comprehensive evaluation. The benchmark comprises 44,872 questions covering ten major risk categories and 76 fine-grained risk points, including high-risk dimensions such as malicious content generation and jailbreak attacks. In addition, this paper introduces an automated evaluation framework based on multi-model judgment. Experimental results demonstrate that this mechanism significantly improves both accuracy and reliability: compared with single-model judgment (GPT-4o, 0.973 accuracy), the proposed multi-model framework achieves 0.986 accuracy while maintaining a similar evaluation time (~1 h) and exhibits strong consistency with expert annotations. Furthermore, adversarial robustness experiments show that our synthesized attack data effectively increases the attack success rate across multiple LLMs, such as from 14.76% to 27.60% on GPT-4o and from 18.24% to 30.35% on Qwen-2.5-7B-Instruct, indicating improved sensitivity to security risks. The proposed unified scoring metric system enables comprehensive model comparison; summarized ranking results show that GPT-4o achieves consistently high scores across ten safety and security dimensions (e.g., 96.26 in ELR, 97.63 in PSI), while competitive open-source models such as Qwen2.5-72B-Instruct and DeepSeek-V3 also achieve strong performance (e.g., 96.70 and 97.63 in PSI, respectively). Although all models demonstrate strong alignment in the safety dimension, they exhibit pronounced weaknesses in security—particularly against jailbreak and adversarial attacks—highlighting critical vulnerabilities and providing actionable direction for future model hardening. This work provides a comprehensive, scalable solution and high-quality data support for automated evaluation of LLMs. Full article

The ordinary Portland cement (OPC) manufacturing process is highly resource-intensive and contributes to over 5% of global CO₂ emissions, thereby contributing to global warming. In this context, researchers are increasingly adopting geopolymers concrete due to their environmentally friendly production process. For decades, industrial byproducts such as fly ash, ground-granulated blast-furnace slag, and silica fume have been used as the primary binders for geopolymer concrete (GPC). However, due to uneven distribution and the decline of coal-fired power stations to meet carbon-neutrality targets, these binders may not be able to meet future demand. The UK intends to shut down coal power stations by 2025, while the EU projects an 83% drop in coal-generated electricity by 2030, resulting in a significant decrease in fly ash supply. Like fly ash, slag, and silica fume, natural clays are also abundant sources of silica, alumina, and other essential chemicals for geopolymer binders. Hence, natural clays possess good potential to replace these industrial byproducts. Recent research indicates that locally available clay has strong potential as a pozzolanic material when treated appropriately. This review article represents a comprehensive overview of the various treatment methods for different types of clays, their impacts on the fresh and hardened properties of geopolymer concrete by analysing the experimental datasets, including 1:1 clays, such as Kaolin and Halloysite, and 2:1 clays, such as Illite, Bentonite, Palygorskite, and Sepiolite. Furthermore, this review article summarises the most recent geopolymer-based prediction models for strength properties and their accuracy in overcoming the expense and time required for laboratory-based tests. This review article shows that the inclusion of clay reduces concrete workability because it increases water demand. However, workability can be maintained by incorporating a superplasticiser. Calcination and mechanical grinding of clay significantly enhance its pozzolanic reactivity, thereby improving its mechanical performance. Current research indicates that replacing 20% of calcined Kaolin with fly ash increases compressive strength by up to 18%. Additionally, up to 20% replacement of calcined or mechanically activated clay improved the durability and microstructural performance. The prediction-based models, such as Artificial Neural Network (ANN), Multi Expression Programming (MEP), Extreme Gradient Boosting (XGB), and Bagging Regressor (BR), showed good accuracy in predicting the compressive strength, tensile strength and elastic modulus. The incorporation of clay in geopolymer concrete reduces reliance on industrial byproducts and fosters more sustainable production practices, thereby contributing to the development of a more sustainable built environment. Full article

Recycling construction and demolition (C&D) waste into recycled aggregate (RA) and recycled aggregate concrete (RAC) is conducive to natural resource conservation and industry decarbonization, which have been attracting much attention from the community. This paper aims to present a synthesis of recent scientific insights on RA and RAC by conducting a systematic review of the latest advances in their properties, test techniques, modeling, modification and improvement, as well as applications. Over 100 papers published in the past three years were examined, extracting enlightening information and recommendations for engineering. The review shows that consistent conclusions have been drawn about the physical properties in that RA can reduce the workability and the setting time of fresh RAC and increase the porosity of hardened RAC. Its impact on drying and autogenous shrinkage is governed by its size and the strength of the parent concrete. RA generally acts negatively on the durability and mechanical properties of concrete, but such effects remain controversial as many opposite observations have been reported. Apart from the commonly used multiscale test techniques, real-time monitoring also plays an important role in the investigation of deformation and fracture processes. Analytical models for RAC were usually modified from the existing models for NAC or established through regression analysis, while for numerical models, the distribution of attached mortar should be considered to improve their accuracy. Machine learning models are effective in predicting RAC properties. Modification of RA can be implemented by either removing or strengthening the attached mortar, while the modification of RAC is mainly achieved by improving its microstructure. Current exploration of RAC applications mainly focuses on the optimization of concrete design and mix procedures, structural components, as well as multifunctional construction materials, revealing the room for its further exploitation in the industry. Full article

China’s urbanization process has entered the stage of mid-to-late transformation and upgrading, with the urbanization and population growth rates having passed the turning point. Urban renewal has become an increasingly important issue, among which the renovation of old residential areas holds enormous potential. The improvement of the living environment is urgent, and enhancing the microclimate to improve the livability and comfort of outdoor residential spaces is a critical factor. This study presents for the first time a quantitative framework for multifactor synergistic optimization by coupling building layout closure and material albedo effects. This paper takes typical old residential areas in Fuyang as an example and uses 3D microclimate simulation software (ENVI-met Version 4.3) to establish a simulation model. It evaluates the microclimate and thermal comfort under different building layouts, green infrastructures, building envelope materials, and various surface materials. The results show that: (1) Regarding building layout, the point-cluster layout generally results in the best improvement of daily cumulative physiological equivalent temperature (PET) values, followed by row-type and enclosed layouts; (2) The optimal solutions for improving the daily average PET value are as follows: using glass as the building envelope material in the point-cluster layout; 100% tree coverage in the row-type layout; and 100% asphalt coverage as the surface material in the point-cluster layout. These three conditions reduce the daily average PET by 3.51 °C, 23.87 °C, and 2.65 °C, respectively; (3) The degree of impact on PET is ranked as: green infrastructure configuration > building layout > building envelope materials > surface materials; (4) When the building layout of the residential area is more enclosed, such as using row-type or enclosed layouts, the order of building envelope materials improving thermal comfort is: brick, concrete, and glass. When the building layout is less enclosed, such as using point-cluster layouts, the order of building envelope materials improving thermal comfort is: glass, brick, and concrete. Therefore, it is concluded that applying point-cluster layout in buildings, using glass as the building envelope material, and having 100% coverage of asphalt pavement as the surface material and 100% coverage of trees can maximize the improvement of the thermal environment of the buildings. The conclusion is applicable to old residential areas in warm temperate semi-humid monsoon climatic zones characterized by high densities (floor area ratios > 2.5) and high rates of hardening of the ground (≥80%), and is particularly instructive for medium-sized urban renewal projects with an urbanization rate between 45% and 60%. Full article

The surface of a whole-roll flatness roll is in long-term contact with the steel strip, leading to slipping and wear and placing higher demands on the performance of the roll surface. This study establishes a finite element model for moving induction quenching and a phase transformation hardness numerical model by generating multi-field simulations and hardness predictions for the flatness roll during induction quenching. First, the thermal–physical properties of the roll material, MC3, are calculated using JMatPro V13.0. The dynamic domain and moving mesh techniques are applied in COMSOL Multiphysics to simulate time-varying boundary conditions, and the JMAK and K-M phase transformation models are used for electromagnetic–thermal–microstructure field simulations. Subsequently, the Taguchi method is used to optimize the induction quenching process of the flatness roll. After optimization, the martensitic hardened layer depth along the axial direction of the roll becomes uniformly distributed near the target value of 3 mm. Finally, through the modified Maynier hardness model, the corrected formula for the Vickers hardness of MC3 is obtained. The calculated hardness value of the roll surface in the simulation model reaches 950 HV, which agrees well with the experimental hardness results, validating the ability of the numerical model to guide specific processes. Full article

The problem of early crack formation caused by temperature stresses in hardening concrete is very relevant for massive monolithic reinforced concrete structures. The aim of the work is to develop a method for thermal cracking risk prediction during the construction of massive monolithic reinforced concrete structures. The innovation of the research consists in taking into account the dependence of the concrete elastic modulus and strength on the time and temperature of hardening. The significance of the study lies in analysis of methods for reducing the risk of early cracking using the example of a real structure. The object of the study is a fragment of a massive monolithic dock wall. The analysis is performed by the finite element method using a program developed by the authors in the MATLAB environment. Verification of the developed software was performed by comparison with the solution in ANSYS using a linear elastic model without time dependence of the elastic modulus. Next, various options were used to set the dependence of the mechanical characteristics of concrete on the time and temperature of hardening. An analysis was conducted of the possibility of reducing the risk of early cracking by reducing the length of the concrete block, correcting the heat exchange conditions of the surfaces, and reducing the heat generation of concrete. Full article

This work shows the effect of artificial aging on the mechanical properties of an API 5L Gr. B steel that undergoes different artificial aging times (0, 500, 1250, and 1500 h). Among the mechanical properties studied are the stress–strain curve, yield stress, and ultimate tensile stress. In addition, the parameters from the Ludwik–Hollomon equation, which are the parameter (K) and the work-hardening coefficient (n), are obtained for the true stress–strain curve. Once the true stress–strain curve is plotted, a 3D model of a transverse crack in a pipeline is proposed. The crack defects are straight, and the finite element method (FEM) is used to determine its behavior at different sizes in order to estimate the failure pressure. It can be said that the mechanical properties (stress–strain curve) increase for an aging time of 500 h compared to the air condition because of the over-aging process, something that is well recognized in the literature. In general, past the over-aging condition, with an increase in the aging time, the mechanical properties tend to decrease. This behavior is similar for the failure pressure. The FEM is sensitive to the decrease in mechanical properties along with the true stress–strain curve. Full article

The real-time, full-field simulation of the tube hydroforming process is crucial for deformation monitoring and the timely prediction of defects. However, this is rather difficult for finite-element simulation due to its time-consuming nature. To overcome this drawback, in this paper, a surrogate model framework was proposed by integrating the finite-element method (FEM) and machine learning (ML), in which the basic methodology involved interrupting the computational workflow of the FEM and reassembling it with ML. Specifically, the displacement field, as the primary unknown quantity to be solved using the FEM, was mapped onto the displacement boundary conditions of the tube component with ML. To this end, the titanium tube material as well as the hydroforming process was investigated, and a fairly accurate FEM model was developed based on the CPB06 yield criterion coupled with a simplified Kim–Tuan hardening model. Numerous FEM simulations were performed by varying the loading conditions to generate the training database for ML. Then, a random forest algorithm was applied and trained to develop the surrogate model, in which the grid search method was employed to obtain the optimal combination of the hyperparameters. Sequentially, the principal strain, the effective strain/stress, as well as the wall thickness was derived according to continuum mechanics theories. Although further improvements were required in certain aspects, the developed FEM-ML surrogate model delivered extraordinary accuracy and instantaneity in reproducing multi-physical fields, especially the displacement field and wall-thickness distribution, manifesting its feasibility in the real-time, full-field simulation and monitoring of deformation states. Full article

The ductility and exhibition of the multiple, fine, self-controlled cracking of strain-hardening cementitious composites (SHCCs) under tension has made them attractive for enhancing the durability of civil infrastructure. These fine cracks are key to preventing the ingress of water and harmful chemicals into the structure and thereby achieving steel reinforcement. However, several studies have suggested that the short-term fine cracks shown in the laboratory may end up exceeding the acceptable crack widths that are specified in design codes when SHCC members are subjected to sustained constant loads. In real structures, however, the load is also shared by the steel reinforcement in the member, so the SHCC within may not be under a constant load; therefore, the crack widening will not be as severe. This study focuses on the creep behaviour of SHCCs when they are applied as an external layer on reinforced concrete to enhance durability. A novel approach to simulate various stress–strain regimes in such systems is developed by using a fixture to share a sustained moment exclusively between a reinforcement member and SHCC. The developed load-sharing system allows stresses within the reinforcement and SHCC to be monitored against time during the imposed loading, while ensuring access to the SHCC layer for instrumentation and monitoring of strain/cracking. The time-dependent widening of cracks in the SHCC layer is found to be much less significant than that under constant loading, so resistance to water/chemical penetration can still be ensured in the long term. The obtained information on the variation in stress, strain, and crack opening with time will be useful for the development of a general model for the creep behaviour of SHCC members. Full article