Search Results (192)

This paper evaluates the time-dependent fragility and post-earthquake residual seismic performance of existing steel frame columns in offshore atmospheric environments. Based on experimental research, the seismic failure mechanism and deterioration laws of the seismic behavior of corroded steel frame columns were revealed. A finite element analysis (FEA) method for steel frame columns, which considers corrosion damage and ductile metal damage criteria, is developed and validated. A parametric analysis in terms of service age and design parameters is conducted. Considering the impact of environmental erosion and aging, a classification criterion for damage states for existing steel frame columns is proposed, and the theoretical characterization of each damage state is provided based on the moment-rotation skeleton curves. Based on the test and numerical analysis results, probability distributions of the fragility function parameters (median and logarithmic standard deviation) are constructed. The evolution laws of the fragility parameters with increasing service age under each damage state are determined, and a time-dependent fragility model for existing steel frame columns in offshore atmospheric environments is presented through regression analysis. At a drift ratio of 4%, the probability of complete damage to columns with 40, 50, 60, and 70-year service ages increased by 18.1%, 45.3%, 79.2%, and 124.5%, respectively, compared with columns within a 30-year service age. Based on the developed FEA models and the damage class of existing columns, the influence of characteristic variables (service age, design parameters, and damage level) on the residual seismic capacity of earthquake-damaged columns, namely the seismic resistance that can be maintained even after suffering earthquake damage, is revealed. Using the particle swarm optimization back-propagation neural network (PSO-BPNN) model, nonlinear mapping relationships between the characteristic variables and residual seismic capacity are constructed, thereby proposing a residual seismic performance evaluation model for existing multi-aged steel frame columns in an offshore atmospheric environment. Combined with the damage probability matrix of the time-dependent fragility, the expected values of the residual seismic capacity of existing multi-aged steel frame columns at a given drift ratio are obtained directly in a probabilistic sense. The results of this study lay the foundation for resistance to sequential earthquakes and post-earthquake functional recovery and reconstruction, and provide theoretical support for the full life-cycle seismic resilience assessment of existing steel structures in earthquake-prone areas. Full article

The instability of mine roadways is significantly influenced by the coupled effects of groundwater seepage and non-uniform loading. These interactions often induce localized plastic deformation and progressive failure, particularly in the roof and sidewall regions. Seepage elevates pore water pressure and deteriorates the mechanical properties of the rock mass, while non-uniform loading leads to stress concentration. The combined effect facilitates the propagation of microcracks and the formation of shear zones, ultimately resulting in localized instability. This initial damage disrupts the mechanical equilibrium and can evolve into severe geohazards, including roof collapse, water inrush, and rockburst. Therefore, understanding the damage and failure mechanisms of mine roadways at the mesoscale, under the combined influence of stress heterogeneity and hydraulic weakening, is of critical importance based on laboratory experiments and numerical simulations. However, the large scale of in situ roadway structures imposes significant constraints on full-scale physical modeling due to limitations in laboratory space and loading capacity. To address these challenges, a straight-wall circular arch roadway was adopted as the geometric prototype, with a total height of 4 m (2 m for the straight wall and 2 m for the arch), a base width of 4 m, and an arch radius of 2 m. Scaled physical models were fabricated based on geometric similarity principles, using defect-bearing sandstone specimens with dimensions of 100 mm × 30 mm × 100 mm (length × width × height) and pore-type defects measuring 40 mm × 20 mm × 20 mm (base × wall height × arch radius), to replicate the stress distribution and deformation behavior of the prototype. Uniaxial compression tests on water-saturated sandstone specimens were performed using a TAW-2000 electro-hydraulic servo testing system. The failure process was continuously monitored through acoustic emission (AE) techniques and static strain acquisition systems. Concurrently, FLAC^3D 6.0 numerical simulations were employed to analyze the evolution of internal stress fields and the spatial distribution of plastic zones in saturated sandstone containing pore defects. Experimental results indicate that under non-uniform loading, the stress–strain curves of saturated sandstone with pore-type defects typically exhibit four distinct deformation stages. The extent of crack initiation, propagation, and coalescence is strongly correlated with the magnitude and heterogeneity of localized stress concentrations. AE parameters, including ringing counts and peak frequencies, reveal pronounced spatial partitioning. The internal stress field exhibits an overall banded pattern, with localized variations induced by stress anisotropy. Numerical simulation results further show that shear failure zones tend to cluster regionally, while tensile failure zones are more evenly distributed. Additionally, the stress field configuration at the specimen crown significantly influences the dispersion characteristics of the stress–strain response. These findings offer valuable theoretical insights and practical guidance for surrounding rock control, early warning systems, and reinforcement strategies in water-infiltrated mine roadways subjected to non-uniform loading conditions. Full article

As the global energy transition moves towards the goal of low-carbon sustainability, it is crucial to build a new energy power system. The performance and reliability of Smart Circuit Breakers are the key to ensuring safe operation. The control circuit is the key to the reliability of Smart Circuit Breakers, so studying its performance-degradation process is of great significance. This study centers on the development of a degradation model and the performance-degradation-assessment method for the control circuit of Smart Circuit Breakers and proposes a novel approach for lifetime prediction. Firstly, a test platform is established to collect necessary data for developing a performance-degradation model based on the two-stage Wiener process. According to the theory of maximum likelihood estimation and Schwarz information criterion, the estimation method of model distribution parameters in each degradation stage and the degradation ‘turning point’ method are studied. Then, reliability along with residual life serve as evaluation criteria for analyzing the control circuit’s performance deterioration. Taking the degradation characteristic data into the degradation model, for example, analysis, combined with the Arrhenius empirical formula, the reliability function at room temperature and the curve of the residual life probability density function is obtained. Ultimately, the average service life of the Smart Circuit Breaker control circuit at room temperature is 178,100 h (20.3 years), with a degradation turning point at 155,000 h (17.7 years), providing a basis for the lifetime evaluation of low-voltage circuit breakers. Full article

Background: While early risk stratification in STEMI is essential, the threat of cardiogenic shock (CS) persists after revascularization due to reperfusion injury and evolving instability. However, risk prediction in later phases—after revascularization—is less explored, despite its importance in guiding intensive care decisions. This study evaluates machine learning (ML) models for dynamic risk assessment in interventional cardiology and cardiac intensive care unit (CICU) phases, where timely detection of deterioration can guide treatment escalation. Methods: We retrospectively analyzed clinical and procedural data from 158 patients diagnosed with STEMI complicated by cardiogenic shock, treated between 2019 and 2022 at the Cardiology Department of the University Emergency Hospital of Bucharest, Romania. Machine learning models—Random Forest (RF), and Quadratic Discriminant Analysis (QDA)—were developed and tested specifically for the interventional cardiology and CICU phases. Model performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), accuracy (ACC), sensitivity, specificity, and F1-score. Results: In the interventional phase, RF and QDA achieved the highest accuracy, both reaching 87.50%. In the CICU, RF and QDA demonstrate the best performance, reaching ACCs of 0.843. QDA maintained consistent performance across phases. Relevant predictors included reperfusion strategy, TIMI flow before percutaneous coronary intervention (PCI), Killip class, creatinine, and Creatine Kinase Index (CKI)—all parameters routinely assessed in STEMI patients. These models effectively identified patients at risk for post-reperfusion complications and hemodynamic decline, supporting decisions regarding extended monitoring and ICU-level care. Conclusions: Predictive models implemented in advanced STEMI phases can contribute to dynamic, phase-specific risk reassessment and optimize CICU resource allocation. These findings support the integration of ML-based tools into post-PCI workflows, enabling earlier detection of clinical decline and more efficient deployment of intensive care resources. When combined with earlier-stage models, the inclusion of interventional and CICU phases forms a dynamic, end-to-end risk assessment framework. With further refinement, this system could be implemented as a mobile application to support clinical decisions throughout the STEMI care continuum. Full article

Multi-stage axial compressors play a crucial role in aerospace propulsion systems, as their exit flow field characteristics directly impact engine performance and stability. This study conducted numerical simulations on the first 3.5 stages of the NASA 74A transonic multi-stage axial compressor to analyze the exit flow field characteristics under different typical operating conditions. The research primarily investigated airflow deflection angle, radial velocity distribution, and their variation patterns. Additionally, the effects of inlet airflow angle and pressure variations on the exit flow field under non-uniform inlet conditions were examined in detail. The results indicate that at 68% rotational speed, the exit flow field of the NASA 74A compressor deteriorates significantly, with noticeable changes in distribution patterns. For the other four operating conditions, as the rotational speed decreases, both velocity and airflow angle exhibit a positive correlation with rotational speed. Compared to the design condition, peak velocity decreases by 2%, 3.7%, and 7%, while airflow deflection angle changes remain within 3°. Under non-uniform inlet conditions, when the inlet airflow angle decreases from 90° to 70°, variations in peak and average exit velocities remain within 2%, and the changes in peak and average airflow deflection angles are within 1%. However, when the inlet airflow angle decreases from 90° to 70°, the curve of the airflow deflection angle exhibits a leftward shift, with a deviation of 2.6%. Meanwhile, changes in inlet pressure under non-uniform conditions have a relatively minor impact on the overall flow field but significantly affect local distributions. When the inlet pressure increases from 1 atm to 1.05 atm, peak velocity increases by 0.98%, and average velocity rises by 3%. The maximum velocity difference reaches 6%, while the average airflow deflection angle differs by 0.7%, with a maximum deviation of 1.9°. Overall, the compressor exit flow field undergoes significant variations under different operating conditions, with increased flow instability at lower rotational speeds leading to flow separation, low-energy fluid accumulation, and non-uniform pressure distribution. In contrast, non-uniform inlet conditions have a relatively minor effect on the overall flow field but induce noticeable local changes, providing theoretical insights for compressor design optimization and performance evaluation. Full article

As a primary construction material, concrete plays a vital role in the development of infrastructure, including bridges, highways, and large-scale buildings. In Northeast China, the structural integrity of concrete faces severe challenges due to freeze–thaw cycles and substantial diurnal temperature variations. This study involved a thorough examination of concrete’s performance under varying numbers of temperature differential cycling (60 to 300) and freeze–thaw cycles (75 to 300). The results showed that both freeze–thaw and temperature differential cycling led to increasing mass loss with the number of cycles. Peak mass losses reached 3.1% and 1.2% under freeze–thaw and temperature differential cycles, respectively, while the combined action resulted in a maximum mass loss of 4.1%. The variation trends in dynamic elastic modulus and compressive strength differed depending on the environmental conditions. Under identical freeze–thaw cycling, both properties exhibited an initial increase followed by a decrease with increasing temperature differential cycles. After 120 temperature differential cycles, the dynamic modulus and compressive strength increased by 4.7–6.2% and 7.5–10.9%, respectively. These values returned to near their initial levels after 180 cycles and further decreased to reductions of 17.0–22.6% and 15.3–29.4% by the 300th cycle. In contrast, under constant temperature differential cycles, dynamic modulus and compressive strength showed a continuous decline with increasing freeze–thaw cycles, reaching maximum reductions of 5.0–11.5% and 18.1–31.8%, respectively. Notably, the combined effect of temperature differential and freeze–thaw cycles was significantly greater than the sum of their individual effects. Compared to the superposition of separate effects, the combined action amplified the losses in dynamic modulus and compressive strength by factors of up to 3.7 and 1.8, respectively. Additionally, the fatigue life of concrete subjected to combined temperature differential and freeze–thaw cycles followed a two-parameter Weibull distribution. Analysis of the S-N_f curves revealed that the coupled environmental effects significantly accelerated the deterioration of fatigue performance. Full article

Corrosion significantly contributes to the deterioration of reinforced concrete (RC) structures. This work investigates its impact on the seismic behaviour of RC buildings. A simplified numerical simulation strategy was developed and validated, analysing two columns with corrosion rates of 0% and 20%, based on existing experimental research found in the literature. Subsequently, five distinct scenarios were developed, incorporating various corrosion rates of 0%, 10%, and 20%, applied to a structure designed in accordance with the Eurocode 8. Nonlinear pushover analyses were conducted to derive capacity curves and bilinear curves, focusing on key parameters such as maximum strength and corresponding drift, initial stiffness, secant stiffness, yield force and drift. Displacement and drift profiles per floor were analysed at the significant damage performance point (SD). The results indicate a clear negative impact of corrosion on structural performance, evidenced by reduced capacity to withstand deformations and lateral forces, alongside an increased likelihood of damage to non-structural elements. Full article

This study investigates the effects of a vibration force field on the mixing and structural properties of polylactic acid (PLA), polybutylene succinate (PBS), and ethylene–glycidyl methacrylate terpolymer (EGMA) blends. A balanced triple-screw dynamic extrusion process was utilized to prepare PLA/PBS/EGMA composites under various vibration parameters, specifically amplitude and frequency. The results indicate that the introduction of a vibration force field significantly enhances the dispersion of the PLA/PBS/EGMA blend, leading to improved mechanical properties, thermal stability, and crystallization behavior. When the vibration frequency was 6 Hz and the amplitude was 1.0 mm, the impact strength increased from the steady-state value of 70.86 KJ/m² to 88.21 KJ/m². When the amplitude was 0.4 mm and the frequency was 10 Hz, the impact strength reached 81.86 KJ/m². The orthogonal experimental design and entropy method analysis revealed that vibration frequency and amplitude play a dominant role in optimizing mechanical performance, whereas processing temperature and rotor speed exhibit minimal impact. Scanning electron microscopy (SEM) analysis confirmed that the vibration force field reduces phase separation, promoting a finer and more homogeneous dispersion of PBS and EGMA within the PLA matrix. Additionally, TGA and DTG curves suggest that when the vibration amplitude and frequency are lower than specific thresholds, the thermal stability of the blend deteriorates. In contrast, when they exceed those thresholds, thermal stability improves. For instance, with an amplitude of 1.0 mm, the initial degradation temperature (T5) climbs from 328.6 °C to 333.7 °C. At a frequency of 10 Hz, T5 reaches 333.1 °C. These findings provide theoretical support for the application of vibration-assisted extrusion in the development of high-performance biodegradable polymer blends. Full article

Background/Objectives: Data availability can affect the performance of AI-based early warning scores (EWSs). This study evaluated how the extent of missing data and imputation strategies influence the predictive performance of the VitalCare–Major Adverse Event Score (VC-MAES), an AI-based EWS that uses last observation carried forward and normal-value imputation for missing values, to forecast clinical deterioration events, including unplanned ICU transfers, cardiac arrests, or death, up to 6 h in advance. Methods: We analyzed real-world data from 6039 patient encounters at Keimyung University Dongsan Hospital, Republic of Korea. Performance was evaluated under three scenarios: (1) using only vital signs and age, treating all other variables as missing; (2) reintroducing a full set of real-world clinical variables; and (3) imputing missing values drawn from a distribution within one standard deviation of the observed mean or using Multiple Imputation by Chained Equations (MICE). Results: VC-MAES achieved the area under the receiver operating characteristic curve (AUROC) of 0.896 using only vital signs and age, outperforming traditional EWSs, including the National Early Warning Score (0.797) and the Modified Early Warning Score (0.722). Reintroducing full clinical variables improved the AUROC to 0.918, whereas mean-based imputation or MICE decreased the performance to 0.885 and 0.827, respectively. Conclusions: VC-MAES demonstrates robust predictive performance with limited inputs, outperforming traditional EWSs. Incorporating actual clinical data significantly improved accuracy. In contrast, mean-based or MICE imputation yielded poorer results than the default normal-value imputation, potentially due to disregarding the “informative presence” embedded in missing data patterns. These findings underscore the importance of understanding missingness patterns and employing imputation strategies that consider the decision-making context behind data availability to enhance model reliability. Full article

With increasing service life, concrete durability gradually deteriorates, requiring urgent repair and reinforcement. Conventional cement-based repair materials exhibit disadvantages such as high brittleness, low tensile strength, poor adhesion, and insufficient durability, making them inadequate for high-quality structural repairs. Based on the molecular structure–activity relationship, this study developed a novel waterborne epoxy–cement-based composite repair material using self-synthesized waterborne epoxy resin (WEP). The mechanism by which WEP improves the performance of cement-based materials was elucidated. The results indicate that WEP significantly influenced the early formation of silicate crystals. Furthermore, the addition of WEP enhanced material flexibility and adhesion, achieving flexural strength of 12.9 MPa and direct tensile bond strength of 2.13 MPa at 28 days, representing increases of approximately 30% and 58%, respectively, compared to the control group. Stress–strain curve analysis revealed that the ultimate strain of WEP-modified cement mortar reached 0.024%. SEM analysis revealed that cured WEP formed a dense cross-linked network with cement hydration products. This microstructural modification refined the pore structure, effectively addressing the material’s brittleness, ductility, and durability limitations. Full article

Background/Objectives: It is essential to identify acute myocardial infarction patients with greater risk of deterioration following primary percutaneous coronary intervention. Due to an inconsistent result about predictors of 30-day outcomes regarding scoring systems for the first episode of acute myocardial infarction, the objective of this study is to develop novel scoring systems to predict 30-day mortality among patients with a first episode of acute myocardial infarction who underwent primary percutaneous coronary intervention. Methods: This retrospective study was conducted with total sampling for all patients with first-time acute myocardial infarction who underwent primary percutaneous coronary intervention between 2021 and 2024 at Dr. Soetomo Hospital, Indonesia. We performed a total sampling and collected 1714 patients, of which 1535 patients were included. Our primary outcomes included 30-day mortality. Results: The analysis included 1535 patients: 926 in the derivation set and 609 in the validation set. In our study, the 30-day mortality rate was 20.7%. Multivariate logistic regression analysis was used to build prediction models in the derivation group and then validated in the validation cohort. The area under the ROC curve of the RURUS SURYAWAN score to predict 30-day mortality was 0.944 (0.906–0.972) in the derivation set and 0.959 (0.921–0.983) in the validation set, with 94.6% sensitivity and 97.3% specificity (p < 0.001). Conclusions: After adjusting for potential confounders, we developed RURUS SURYAWAN, a novel scoring system to identify predictors of 30-day mortality among acute myocardial infarction before primary percutaneous coronary intervention. Full article

Through alternating high–low temperature and humid heat tests, six sets of different humidity cycle numbers were applied to Q235B low-carbon steel and Q345B low-alloy steel. Monotonic tensile tests were conducted to compare the differences in monotonic performance degradations. The influence of humidity cycle numbers on the hysteretic and fatigue performance of Q235 steel was investigated through cyclic loading tests. A cyclic constitutive model based on the mixed hardening model was established and validated. The results show that the humid heat environment causes corrosion of the steel, and the degree of corrosion follows a power-law relationship with the number of humid heat cycles. Under monotonic loading, as the number of humid heat cycles increases, the strength and deformation performance of both steels degrade linearly, with Q345 low-alloy steel exhibiting more significant performance deterioration. The corrosion damage induced by the humid heat environment greatly reduces the low-cycle fatigue life of Q235 steel, and the more severe the corrosion, the lower the fatigue life. However, there is no significant effect on the development of the hysteretic curve shape. Under variable amplitude cyclic loading, as the corrosion degree increases, the hysteretic energy dissipation and energy dissipation rate continuously decrease. The two-segment backbone curve considering mass loss rate and the material hardening parameters based on the mixed hardening model can accurately describe the hysteretic characteristics of Q235 low-carbon steel under the humid heat environment. Full article

Freeze–thaw action is a key factor in the deterioration of the dynamic mechanical behavior of rocks in cold regions. This study used yellow sandstone, which is prevalent in the seasonally cold region of Xinjiang, China. The yellow sandstone samples were subjected to various temperatures and a range of freeze–thaw cycles. Impact mechanical tests were performed using a Split Hopkinson Pressure Bar (SHPB) system on the treated samples. The effects of freezing temperature and changes in impact load on the mechanical properties of frozen–thawed sandstone were examined. Additionally, the damage fractal characteristics of the sandstone were analyzed using fractal theory. The results indicate that as the freezing temperature decreases, the stress–strain curves of frozen–thawed specimens exhibit a clear initial compaction stage. The dynamic strength of the specimens decreases with lower freezing temperatures and shows a logarithmic relationship with the loading strain rate; however, the dynamic deformation modulus exhibits no significant correlation with the strain rate. The fractal dimension is positively correlated with the strain rate, indicating that lower freezing temperatures correspond to a higher rate of increase in the fractal dimension. These findings offer valuable insights into the damage deterioration characteristics of frozen–thawed rocks under varying temperature conditions. Full article

The current transformer (CT)-based energy harvesting method has gained considerable attention for low-power devices. Accurate estimation of the B-H curve is essential to develop a high-performance CT, as it closely relates to the electromagnetic behavior of CT material. However, the existing estimation methods for the B-H curve face several drawbacks, which include process complexity and a high cost. This study presented an intuitive method to estimate the B-H curve based on the experimentally obtained resistance-voltage data. The performance of the CT core is obtained based on the estimated B-H curve, which exhibited an error of only 2.6% when compared to the experimental results for the most accurate case. Additionally, we analyzed split-core performance deterioration caused by the presence of an air gap. The air gap formation of the split core was closely related to the surface roughness, which significantly influenced core performance. The air gap range that minimizes the reduction in performance is predicted and validated through simulations and experiments. This research highlights a straightforward approach to obtaining the B-H curve of magnetic CT core material. We believe that this study provides the design guidelines needed to develop a high-performance CT core, including considerations for core geometry and the recommended air gap range. Full article

Background Predictive machine learning models have made use of a variety of scoring systems to identify clinical deterioration in ICU patients. However, most of these scores include variables that are dependent on medical staff examining the patient. We present the development of a real-time prediction model using clinical variables that are digital and automatically generated for the early detection of patients at risk of deterioration. Methods Routine monitoring data were used in this analysis. ICU patients with at least 24 h of vital sign recordings were included. Deterioration was defined as qSOFA ≥ 2. Model development and validation were performed internally by splitting the cohort into training and test datasets and validating the results on the test dataset. Five different models were trained, tested, and compared against each other. The models were an artificial neural network (ANN), a random forest (RF), a support vector machine (SVM), a linear discriminant analysis (LDA), and a logistic regression (LR). Results In total, 7156 ICU patients were screened for inclusion in the study, which resulted in models trained from a total of 28,348 longitudinal measurements. The artificial neural network showed a superior predictive performance for deterioration, with an area under the curve of 0.81 over 0.78 (RF), 0.78 (SVM), 0.77 (LDA), and 0.76 (LR), by using only four vital parameters. The sensitivity was higher than the specificity for the artificial neural network. Conclusions The artificial neural network, only using four automatically recorded vital signs, was best able to predict deterioration, 10 h before documentation in clinical records. This real-time prediction model has the potential to flag at-risk patients to the healthcare providers treating them, for closer monitoring and further investigation. Full article