Search Results (1,173)

This study investigated the effects of rubber substitution ratios (0%, 5%, 10%, 15%) on the frost resistance of rubber concrete with recycled brick–concrete aggregate (BRC). The freeze–thaw (F–T) damage model was established and improved, and the damage mechanism was revealed. The results showed that with the increase in rubber substitution ratio, the frost resistance indices of BRC did not improve or decline synchronously. An increase in rubber content could enhance one index, such as the relative compressive strength, but was often achieved at the expense of reductions in other indices, such as the relative dynamic elastic modulus (RDEM) and relative quality. Consequently, a single indicator was insufficient for evaluating the overall frost resistance. To address this limitation, an entropy weight-based evaluation system was developed. This system integrated the multiple indices into a unified damage score. When combined with defined damage grades, it enabled a holistic assessment of the damage state. For the nonlinear accelerated damage stage during freeze–thaw cycles, the Weibull distribution-based freeze–thaw damage model demonstrated higher prediction accuracy (R² > 0.85) compared to the conventional freeze–thaw fatigue model. The freeze–thaw damage in BRC originated from the competition between “pore deterioration and crack propagation at weak interfaces” and “the elastic buffering effect of rubber.” This study provided a reference for the frost-resistance design and freeze–thaw life prediction of BRC in cold regions. Full article

Loess subgrades are prone to significant strength reduction and deformation under cyclic traffic loads and moisture ingress. Permeable polyurethane polymer grouting has emerged as a promising non-excavation technique for rapid subgrade reinforcement. This study systematically investigated the fatigue behavior of polymer-grouted loess using laboratory fatigue tests and numerical simulations. A series of stress-controlled cyclic tests were conducted on grouted loess specimens under varying moisture contents and stress levels, revealing that fatigue life decreased with increasing moisture and stress levels, with a maximum life of 200,000 cycles achieved under optimal conditions. The failure process was categorized into three distinct stages, culminating in a “multiple-crack” mode, indicating improved stress distribution and ductility. Statistical analysis confirmed that fatigue life followed a two-parameter Weibull distribution, enabling the development of a probabilistic fatigue life prediction model. Furthermore, a 3D finite element model of the road structure was established in Abaqus and integrated with Fe-safe for fatigue life assessment. The results demonstrated that polymer grouting reduced subgrade stress by nearly one order of magnitude and increased fatigue life by approximately tenfold. The consistency between the simulation outcomes and experimentally derived fatigue equations underscores the reliability of the proposed numerical approach. This research provides a theoretical and practical foundation for the fatigue-resistant design and maintenance of loess subgrades reinforced with permeable polyurethane polymer grouting, contributing to the development of sustainable infrastructure in loess-rich regions. Full article

While carbon fiber strength is typically characterized through single-fiber tensile tests, these isolated measurements do not account for the local mechanical constraints present within a composite architecture. This study employs a synergistic computational micromechanics approach combining finite element analysis (FEA) and analytical modeling to investigate how the surrounding matrix influences the Stress Intensity Factor (SIF) and the apparent ultimate strength of embedded fibers. By calculating the J-integral, we demonstrate that the matrix provides a significant shielding effect, constraining crack opening displacements and substantially reducing the SIF. This mechanism results in a marked increase in in situ fiber tensile strength relative to dry fibers. Incorporating this matrix-adjusted Weibull distribution into a longitudinal failure model significantly improves the prediction of fiber-break density accumulation, showing closer correlation with experimental benchmarks than traditional models. Full article

Information diffusion models developed for Twitter, Reddit, and Facebook assume network contagion and competition for shared attention. Telegram operates differently. It is built around channels rather than social graphs, and users receive posts directly from subscribed channels without algorithmic mediation. We analyze over 5000 forwarding cascades from the Pushshift Telegram dataset to examine whether existing diffusion models generalize to this broadcast environment. Our findings reveal fundamental structural differences. Telegram forwarding produces perfect star topologies with zero multi-hop propagation. Every forward connects directly to the original message, creating trees with maximum depth of exactly 1. This contrasts sharply with Twitter retweet chains that routinely reach depths of 5 or more hops. Forwarding delays follow heavy-tailed Weibull or lognormal distributions with median delays measured in days rather than hours. Approximately 15 to 20 percent of cascades exhibit administrative bulk reposting rather than organic user-driven growth. Most strikingly, early-stage competitive overtaking is absent. Six of 30 pairs exhibit crossings, but these occur late (median 79 days) via administrative bursts rather than organic competitive acceleration during peak growth. We develop a delay-driven star diffusion model that treats forwarding as independent draws from a delay distribution. The model achieves median prediction errors below 10 percent for organic cascades. These findings demonstrate that platform architecture fundamentally shapes diffusion dynamics. Comparison with prior studies on Twitter, Weibo, and Reddit reveals that Telegram’s broadcast structure produces categorically different patterns—including perfect star topology and asynchronous delays—requiring platform-specific modeling approaches rather than network-based frameworks developed for other platforms. Full article

Accurate monitoring of income inequality is critical for sustainable socioeconomic development and realizing the United Nations Sustainable Development Goals (SDGs). However, assessing inequality for counties continues to be challenging because of the high cost of household surveys and the limited accuracy of traditional nighttime light (NTL) proxies. To address this gap, we develop the Distribution Matching-based Individual Income Inequality Estimation Model (DM-I3EM), which integrates NTL data with household surveys. The model employs a three-stage workflow: logarithmic transformation of NTL data, estimation of Gini coefficients through Weibull distribution fitting, and selection of region-specific regression models, enabling high-resolution mapping and spatiotemporal analysis of county-level income inequality across China. Results show that DM-I3EM achieves superior performance, with an R² of 0.76 in China’s Eastern region (outperforming conventional NTL-based methods, R ≈ 0.5). By overcoming the spatiotemporal gaps of survey data, the model enables full-coverage estimation, revealing a regional divergence in income inequality across China from 2013 to 2022: inequality is intensifying in northern and western counties while stabilizing in the developed southern coastal regions. Furthermore, spatial agglomeration of inequality has strengthened, particularly in coastal urban clusters. These findings highlight emerging risks to socioeconomic sustainability. This study provides a robust, replicable framework for estimating inequality in data-scarce regions, offering policymakers actionable evidence to identify high-risk areas and design targeted strategies for advancing SDG 10 (Reduced Inequalities). Full article

An experimental investigation was conducted to evaluate the statistical properties and scintillation mitigation performance of multi-aperture free-space optical transmission under real-measured atmospheric turbulence. Continuous monitoring of turbulence parameters over a 24 h period showed that the atmospheric coherence length ranged from 3 to 29 cm, indicating that the experimental link operated predominantly under weak-to-moderate turbulence conditions, while a limited number of measurement intervals exhibited relatively strong scintillation and were included for statistical modelling analysis. An 865 m four-channel receiving link was constructed under the measured turbulence conditions to acquire irradiance data for analysis. The results show that the multi-aperture reception significantly suppresses scintillation, reducing the scintillation index from 0.36 to 0.04 under moderate turbulence. The irradiance probability density functions were fitted using lognormal, Gamma–Gamma, exponentiated Weibull, and Málaga (M) distributions. The M distribution exhibited superior adaptability, with fitting accuracy improved by 18.75% under weak turbulence and 13.16% under moderate turbulence. Further analysis shows that the shape parameters of the M distribution vary systematically with turbulence strength, effectively capturing the turbulence-induced evolution of irradiance statistics and providing experimental support for turbulence channel modelling and the optimisation of FSO diversity reception architectures. Full article

Large-Format Additive Manufacturing (LFAM) offers the ability to 3D print composites at multi-meter scale and high throughput by utilizing a screw-based extrusion system that is compatible with pelletized feedstock. As such, LFAM systems like the Big Area Additive Manufacturing (BAAM) system provide a pathway for incorporating AM techniques into industry-scale production. Despite significant growth in LFAM techniques and usage in recent years, typical Multi-Material (MM) techniques induce weak points at discrete material boundaries and encounter a higher frequency of delamination failures. A novel dual-hopper configuration was developed for the BAAM platform to enable in situ switching between material feedstocks that creates a graded transition region in the printed part. This research studied the influence of extrusion screw speed, component design, transition direction, and material viscosity on the transition behavior. Material transitions were monitored using compositional analysis as a function of extruded volume and modeled using a standard Weibull cumulative distribution function (CDF). Screw speed had a negligible influence on transition behavior, but averaging the Weibull CDF parameters of transitions printed using the same configurations demonstrated that designs intended to improve mixing increased the size of the blended material region. Further investigation showed that the relative difference and change in complex viscosity influenced the size of the blended region. These results indicate that tunable properties and material transitions can be achieved through selection and modification of composite feedstocks and their complex viscosities. Full article

The analysis of road traffic accidents often reveals asymmetric patterns, providing insights that support the development of preventive measures, reduce fatalities, and improve road safety interventions. The Rayleigh distribution, a continuous distribution with inherent asymmetry, is well suited for modeling right-skewed data and is widely used in scientific and engineering fields. It also shares structural characteristics with other skewed distributions, such as the Weibull and exponential distributions, and is particularly effective for analyzing right-skewed accident data. This study considers several approaches for constructing confidence intervals, including the percentile bootstrap, bootstrap with standard error, generalized confidence interval, method of variance estimates recovery, normal approximation, Bayesian Markov Chain Monte Carlo, and Bayesian highest posterior density methods. Their performance was evaluated through Monte Carlo simulation based on coverage probabilities and expected lengths. The results show that the HPD method achieved coverage probabilities at or above the nominal confidence level while providing the shortest expected lengths. Finally, all proposed confidence intervals were applied to fatalities recorded during the seven hazardous days of Thailand’s Songkran festival in 2024 and 2025. Full article

A new general class of distributions is proposed by applying the transformation to the random variable that follows the generalized odd-logistic family. Using the proposed family, we introduce a flexible Weibull distribution. The importance of the proposed distribution is demonstrated and compared with different generalizations of the Weibull distribution via three real data applications. A quantile regression model is obtained using the newly developed Weibull model and compared with the standard Weibull quantile regression model through a real data application. Full article

Driven by the dual imperatives of global energy transition and sustainable development goals, lithium-ion batteries, as critical energy storage carriers, have seen the assessment of their lifecycle reliability and durability become a core issue underpinning the sustainable operation of clean energy systems. Grounded in a multidimensional perspective of sustainable development, this study aims to establish a quantifiable and monitorable battery reliability evaluation framework to address the challenges to lifespan and performance sustainability faced by batteries under complex multi-stress coupled operating conditions. Lithium-ion batteries are widely used across various fields, making an accurate assessment of their reliability crucial. In this study, to evaluate the lifespan and reliability of lithium-ion batteries when operating in various coupling stress environments, a multi-stress collaborative accelerated model(MCAM) considering interaction is established. The model takes into account the principal stress effects and the interaction effects. The former is developed based on traditional acceleration models (such as the Arrhenius model), while the latter is constructed through the combination of exponential, power, and logarithmic functions. This study firstly considers the scale parameter of the Weibull distribution as an acceleration effect, and the relationship between characteristic life and stresses is explored through the synergistic action of primary and interaction effects. Subsequently, a multi-stress maximum likelihood estimation method that considers interaction effects is formulated, and the model parameters are estimated using the gradient descent algorithm. Finally, the validity of the proposed model is demonstrated through simulation, and numerical examples on lithium-ion batteries demonstrate that accurate lifetime prediction is enabled by the MCAM, with test duration, cost, and resource consumption significantly reduced. This study not only provides a scientific quantitative tool for advancing the sustainability assessment of battery systems, but also offers methodological support for relevant policy formulation, industry standard optimization, and full lifecycle management, thereby contributing to the synergistic development of energy storage technology across the economic, environmental, and social dimensions of sustainability. Full article

This paper introduces a novel bivariate distribution, referred to as the Bivariate Burr XII Inverse Weibull (BBXII-IW) distribution, constructed via the Marshall–Olkin approach from the univariate Burr XII Inverse Weibull (BXII-IW) distribution. The proposed BBXII-IW model provides a flexible framework for modeling dependent bivariate data, including competing risk scenarios. The key statistical properties of the distribution are derived, and parameter estimation is conducted using the maximum likelihood method. The model’s performance is evaluated using two types of real-world datasets: (1) bivariate data and (2) dependent competing risk data related to diabetic retinopathy. The results demonstrate that the BBXII-IW distribution offers an improved fit compared to existing models, highlighting its flexibility and practical relevance in modeling complex dependent structures. Full article

This study develops a machine-learning-based framework for multiclass soil classification using Cone Penetration Test (CPT) data, aiming to overcome the limitations of traditional empirical Soil Behavior Type (SBT) charts and improve the automation, continuity, robustness, and reliability of stratigraphic interpretation. A dataset of 340 CPT soundings from 26 sites in Shanghai is compiled, and a sliding-window feature engineering strategy is introduced to transform point measurements into local pattern descriptors. An XGBoost-based multiclass classifier is then constructed using fifteen engineered features, integrating second-order optimization, regularized tree structures, and probability-based decision functions. Results demonstrate that the proposed method achieves strong classification performance across nine soil categories, with an overall classification accuracy of approximately 92.6%, an average F1-score exceeding 0.905, and a mean Average Precision (mAP) of 0.954. The confusion matrix, P–R curves, and prediction probabilities show that soil types with distinctive CPT signatures are classified with near-perfect confidence, whereas transitional clay–silt facies exhibit moderate but geologically consistent misclassification. To evaluate depth-wise prediction reliability, an Accuracy Coverage Rate (ACR) metric is proposed. Analysis of all CPTs reveals a mean ACR of 0.924, and the ACR follows a Weibull distribution. Feature importance analysis indicates that depth-dependent variables and smoothed p_s statistics are the dominant predictors governing soil behavior differentiation. The proposed XGBoost-based framework effectively captures nonlinear CPT–soil relationships, offering a practical and interpretable tool for high-resolution soil classification in subsurface investigations. Full article

In lifetime testing, reliably assessing the life performance index of the Weibull distribution under Type I interval-censored data is a critical task. Although maximum likelihood estimation (MLE) is a conventional approach for parameter estimation, closed-form solutions are unavailable for this data type. To address this limitation, four least-squares estimation methods based on data transformation are developed. The proposed estimations can provide closed-form solutions for the Weibull distribution and life performance index. The asymptotic unbiasedness and normality of the proposed estimators are rigorously established. Their effectiveness is further supported by simulation studies. Moreover, the practical relevance of the methods is illustrated with two real-data applications. Full article

This study formulates predictive maintenance of complex technical systems as a constrained multi-layer probabilistic optimization problem that unifies four interdependent analytical paradigms. The mathematical framework integrates: (i) Weibull reliability modelling with parametric lifetime estimation; (ii) Bayesian posterior updating for dynamic adaptation under uncertainty; (iii) nonlinear machine-learning inference for data-driven pattern recognition; and (iv) ontology-based semantic reasoning governed by logical axioms and domain-specific constraints. The four layers are synthesized through a formal orchestration operator, defined as a sequential composition, where each sub-operator is governed by explicit mathematical constraints: Weibull cumulative distribution functions, Bayesian likelihood-posterior relationships, gradient-based loss minimization, and description logic entailment. The system operates within a cognitive digital twin architecture, with orchestration convergence formalized through iterative parameter refinement until consistency between numerical predictions and semantic validation is achieved. The framework is validated through a case study on aircraft wheel-hub crack prediction. The mathematical formulation establishes a rigorous analytical foundation for cognitive predictive maintenance systems applicable to safety-critical technical systems including aerospace, energy infrastructure, transportation networks, and industrial machinery. Full article

The current work focuses on the study of the long-term evaluation of wind potential and energy production for a specific wind farm siting position over a mountainous region in Hellas. It aims to calculate the probability of exceedance of the twenty-year normalized average annual net production of the wind farm based on ground wind measurements coupled with Copernicus ERA5 data via a measure–correlate–predict (MCP) method. The study proposes an integrated long-term wind resource assessment workflow that couples short-term mast data with a twenty-year ERA5 record via a refined MCP procedure including temporal shifting for complex terrain. It introduces a practical uncertainty framework that jointly treats measurement, MCP, and terrain effects through dRIX and propagates these to energy yield using a bin-wise power curve and Weibull weighting. The proposed methodology is both fast and readily available to end-users and provides a realistic estimate of the energy production and long-term wind distribution in the investigated area. The data and assumptions employed in the calculations are given in detail. The uncertainty of the parameters in the estimation of the wind potential of the broader area and the energy calculation is analyzed. The results of the calculations and the probability of exceedance curve of the normalized twenty-year average annual net production of the wind farm summarize all uncertainty sources, delivering bankable long-term energy projections for the specific case study. Full article