Search Results (104)

This paper presents a probabilistic methodology for generating fragility surfaces for low- to mid-rise steel moment-resisting frames (MRFs) under fire-following-earthquake (FFE). The framework integrates nonlinear dynamic seismic analysis, residual deformation transfer, and temperature-dependent fire simulations within a Monte Carlo environment, while explicitly accounting for uncertainties in structural properties, ground motions, and fire simulation. A fiber-based modeling strategy is employed, combining temperature-sensitive steel materials with fatigue and fracture wrappers to capture cyclic deterioration and abrupt failure. This formulation yields earthquake-only and fire-only fragility curves along the surface boundaries, while interior points quantify the joint fragility response under sequential hazards. The methodology is benchmarked against a machine learning (ML) synthesis framework originally developed for earthquake–tsunami applications and extended here to FFE. Numerical results for a three-story steel MRF show excellent agreement (R² > 0.95, RMSE < 0.02) between simulated and ML-generated surfaces, demonstrating both the efficiency and hazard-neutral adaptability of the ML framework for multi-hazard resilience assessment. Full article

Predicting the Remaining Useful Life (RUL) of light aircraft landing gear is complicated by flight-to-flight variability in operational loads, particularly in sensor-free fleets that rely only on mass-and-balance records. This study develops a hybrid physics–data framework to quantify operational-load-driven uncertainty in the main landing gear strut of a Cessna 172. High-fidelity finite-element strain–life simulations were combined with a quadratic Ridge surrogate and a two-layer bootstrap to generate full probabilistic RUL distributions. The surrogate mapped five mass-and-balance inputs (fuel, front seats, rear seats, forward and aft baggage) to per-flight fatigue damage with high accuracy (R² = 0.991 ± 0.013). At the same time, ±3% epistemic confidence bands were attached via resampling. Borgonovo’s moment-independent Δ indices were applied to incremental damage (ΔD) in this context, revealing front-seat mass as the dominant driver of fatigue variability (Δ = 0.502), followed by fuel (0.212), rear seats (0.199), forward baggage (0.141), and aft baggage (0.100). The resulting RUL distribution spanned 9 × 10⁴ to >2 × 10⁶ cycles, with a fleet average of 0.41 million cycles (95% CI: 0.300–0.530 million). These results demonstrate that operational levers—crew assignment, fuel loading, and baggage placement—can significantly extend strut life. Although demonstrated on a specific training fleet dataset, the methodological framework is, in principle, transferable to other aircraft or mission types. However, this would require developing a new, component-specific finite element model and retraining the surrogate using a representative set of mass and balance records from the target fleet. Full article

The growing complexity of distribution-level virtual power plants (VPPs) demands a rethinking of how flexible demand is modeled, aggregated, and dispatched under uncertainty. Traditional optimization frameworks often rely on deterministic or homogeneous assumptions about end-user behavior, thereby overestimating controllability and underestimating risk. In this paper, we propose a behavior-aware, two-stage stochastic dispatch framework for VPPs that explicitly models heterogeneous user participation via integrated behavioral economics and social interaction structures. At the behavioral layer, user responses to demand response (DR) incentives are captured using a Prospect Theory-based utility function, parameterized by loss aversion, nonlinear gain perception, and subjective probability weighting. In parallel, social influence dynamics are modeled using a peer interaction network that modulates individual participation probabilities through local contagion effects. These two mechanisms are combined to produce a high-dimensional, time-varying participation map across user classes, including residential, commercial, and industrial actors. This probabilistic behavioral landscape is embedded within a scenario-based two-stage stochastic optimization model. The first stage determines pre-committed dispatch quantities across flexible loads, electric vehicles, and distributed storage systems, while the second stage executes real-time recourse based on realized participation trajectories. The dispatch model includes physical constraints (e.g., energy balance, network limits), behavioral fatigue, and the intertemporal coupling of flexible resources. A scenario reduction technique and the Conditional Value-at-Risk (CVaR) metric are used to ensure computational tractability and robustness against extreme behavior deviations. Full article

The assessment of fatigue life is important for the design of pavement materials because fatigue cracks are one of the most common types of failure in pavement structures. The fatigue test is commonly used to determine the fatigue life. However, there are lots of uncertainties, such as the construction environment and personal operations, during the fatigue test due to the complexity of the pavement materials. Determining the fatigue life of pavement materials under uncertainty is a challenging task. In this study, considering cement-stabilized cold recycled mixtures (CSCRMs) as an example, an uncertainty quantification (UQ) method based on PyMC3, a novel and powerful probabilistic programming package, was developed to address the uncertainty in fatigue behavior based on fatigue tests. Probabilistic programming was employed to characterize the uncertainty of fatigue life based on fatigue test data and the fatigue life formula. The uncertainty of fatigue life was quantified by determining the unknown coefficient of the fatigue life formula. Two independent datasets for the CSCRM were used to illustrate and verify the developed method. The coefficients of determination (R²) for the prediction results of fatigue life were higher than 0.96, based on the obtained formula and test data. The maximum and average errors of the coefficients determined using the fatigue equation were less than 11% and 7%, respectively. The verification demonstrates that the predicted fatigue life closely agrees with the test data, and the determined coefficients of the fatigue equation are in excellent agreement with prior findings. The developed method avoided complex statistical computations and references. The UQ can evaluate the fatigue life and its uncertainty and significantly enhance the understanding of the fatigue behavior of the CSCRM. Full article

This study proposes an integrated fatigue life assessment methodology to accurately evaluate the time-domain evolution in tubular joint fatigue damage in offshore wind turbine (OWT) jacket structures under long-term combined wind and wave actions. A customized post-processing module was developed via secondary development on the MLife platform, employing a conditional probability distribution model to perform joint probabilistic modeling of measured marine environmental data, thereby establishing a long-term joint wind–wave distribution database. The reconstruction of hotspot stress time histories at the tubular joints was achieved through a hybrid analytical–numerical approach, integrating analytical formulations of nominal stress with a multi-axial stress concentration factor (SCF) matrix. Long-term fatigue damage assessment was implemented using the Palmgren–Miner linear cumulative damage hypothesis, where a weighted summation methodology based on joint wind–wave probability distributions rigorously accounted for the statistical contributions of individual design load cases. An ultimate bearing capacity analysis was also conducted based on S-N fatigue endurance characteristic curves. This research specifically investigates the influence mechanisms of tuned mass dampers (TMDs) on the time-domain-coupled fatigue performance of tubular joints subjected to long-term combined wind and wave loads. Numerical simulations demonstrate that parametrically optimized TMD systems significantly enhance the fatigue life metrics of critical joints in jacket structures. Full article

The mechanical and structural design of railway vehicles is heavily influenced by their lifetime. Because fatigue is a significant factor that impacts the longevity of railway components, it is imperative that the fatigue resistance properties of crucial components, like leaf springs, be thoroughly investigated. This research investigates the fatigue resistance of 51CrV4 steel under bending and axial tension, considering different stress ratios across low-cycle fatigue (LCF), high-cycle fatigue (HCF), and very-high-cycle fatigue (VHCF) regimes, using experimental data collected from this work and prior research. Data included fractographic analyses aiming to help in understanding some of failures for different loads. The presence of geometric discontinuities, such as notches, amplifies stress concentrations, requiring a probabilistic approach to fatigue assessment. To address notch effects, the theory of critical distances (TCD) was employed to evaluate fatigue strength. TCD model was integrated in fatigue statistical models, such as the Walker model (WSN) and the Castillo–Fernández-Cantelli model adapted for mean stress effects (ACFC). Extending the application of the TCD theory, this research provides an improved probabilistic fatigue model that integrates notch sensitivity, mean stress effects, and fatigue regimes, contributing to more reliable design approaches of railway leaf springs or other components produced in 51CrV4 steel. Full article

This study presents a simulation-based damage modeling and fatigue risk assessment of a reusable ceramic matrix composite thruster designed for short-duration, green bipropellant propulsion systems. The thruster is constructed from a fiber-reinforced ultra-high temperature ceramic matrix composite composed of zirconium diboride, silicon carbide, and carbon fibers. Time-resolved thermal and structural simulations are conducted on a validated thruster geometry to characterize the severity of early-stage thermal shock, stress buildup, and potential degradation pathways. Unlike traditional fatigue studies that rely on empirical fatigue constants or Paris-law-based crack-growth models, this work introduces a simulation-derived stress-margin envelope methodology that incorporates ±20% variability in temperature-dependent material strength, offering a physically grounded yet conservative risk estimate. From this, a normalized risk index is derived to evaluate the likelihood of damage initiation in critical regions over the 0–10 s firing window. The results indicate that the convergent throat region experiences a peak thermal gradient rate of approximately 380 K/s, with the normalized thermal shock index exceeding 43. Stress margins in this region collapse by 2.3 s, while margin loss in the flange curvature appears near 8 s. These findings are mapped into green, yellow, and red risk bands to classify operational safety zones. All the results assume no active cooling, representing conservative operating limits. If regenerative or ablative cooling is implemented, these margins would improve significantly. The framework established here enables a transparent, reproducible methodology for evaluating lifetime safety in ceramic propulsion nozzles and serves as a foundational tool for fatigue-resilient component design in green space engines. Full article

Multimodal freight transport systems, integrating maritime, rail, and road modes, play a vital role in modern logistics but face elevated operational, human, and environmental risks due to their complexity and interdependencies. To address the limitations of conventional risk assessment methods, this study proposes a hybrid risk modeling framework that integrates fault tree analysis (FTA), dynamic fault trees (DFTs), and fuzzy logic reasoning. This approach supports the modeling of sequential failures and captures qualitative uncertainties such as human fatigue and inadequate training. The framework incorporates reliability metrics, including Mean Time to Failure (MTTF) and Mean Time Between Failures (MTBF), enabling the quantification of system resilience and identification of critical failure pathways. Application of the model revealed human error, particularly procedural violations, insufficient training, and fatigue, as the dominant risk factor across transport modes. Road transport exhibited the highest probability of risk occurrence (p = 0.9960), followed by rail (p = 0.9937) and maritime (p = 0.9900). By integrating probabilistic reasoning with qualitative insights, the proposed model offers a flexible decision support tool for logistics operators and policymakers, enabling scenario-based risk planning and enhancing system robustness under uncertainty. Full article

This study investigates the mechanical and fatigue behaviour of friction stir composite joints fabricated from an aluminum alloy (AA6082-T6) and a glass fibre-reinforced polymer (Noryl^® GFN2) under different service temperature conditions. The joints were tested under both quasi-static and cyclic loading at three different temperatures (23, 75, and 130 °C). Fracture surfaces were analyzed, and the probabilistic S–N curves were derived using Weibull distribution. Results indicated that increasing the service temperature caused a non-linear decrease in both the quasi-static and fatigue strength of the joints. Compared to room temperature, joints tested at 75 °C and 130 °C showed a 10% and 50% reduction in average tensile strength, respectively. The highest fatigue strength occurred at 23 °C, while the lowest was at 130 °C, in line with the quasi-static results. Fatigue stress-life plots displayed a semi-logarithmic nature, with lives ranging from 10² to 10⁵ cycles for stress amplitudes between 7.7 and 22.2 MPa at 23 °C, 7.2 to 19.8 MPa at 75 °C, and 6.2 to 13.5 MPa at 130 °C. The joints’ failure occurred in the polymeric base material close to joints’ interface, highlighting the critical role of the polymer in limiting joints’ performance, as confirmed by thermal and scanning electron microscopy analyses. Full article

The warehouse picking process is one of the most critical components of logistics operations. Human–robot collaboration (HRC) is seen as an important trend in warehouse picking, as it combines the strengths of both humans and robots in the picking process. However, in current human–robot collaboration frameworks, there is a lack of effective communication between humans and robots, which results in inefficient task execution during the picking process. To address this, this paper considers trust as a communication bridge between humans and robots and proposes the Stackelberg trust-based human–robot collaboration framework for warehouse picking, aiming to achieve efficient and effective human–robot collaborative picking. In this framework, HRC with trust for warehouse picking is defined as the Partially Observable Stochastic Game (POSG) model. We model human fatigue with the logistic function and incorporate its impact on the efficiency reward function of the POSG. Based on the POSG model, belief space is used to assess human trust, and human strategies are formed. An iterative Stackelberg trust strategy generation (ISTSG) algorithm is designed to achieve the optimal long-term collaboration benefits between humans and robots, which is solved by the Bellman equation. The generated human–robot decision profile is formalized as a Partially Observable Markov Decision Process (POMDP), and the properties of human–robot collaboration are specified as PCTL (probabilistic computation tree logic) with rewards, such as efficiency, accuracy, trust, and human fatigue. The probabilistic model checker PRISM is exploited to verify and analyze the corresponding properties of the POMDP. We take the popular human–robot collaboration robot TORU as a case study. The experimental results show that our framework improves the efficiency of human–robot collaboration for warehouse picking and reduces worker fatigue while ensuring the required accuracy of human–robot collaboration. Full article

Free spans of submarine pipelines are prone to be subjected to vortex-induced vibration (VIV) under the action of currents, leading to fatigue damage of submarine pipelines. In the traditional method, the fatigue damage is predicted assuming that the length of free span is a constant. However, the free-span length may vary in time and space due to local scour and sand wave migration in engineering practice. This study proposed probabilistic methods to predict the fatigue life of the free spans by considering the effect of variant span length and span position. Truncated Gaussian, Raileigh and Uniform distributions of span length due to local scour, and a sinusoidal pattern with a constant migration rate is assumed for the sand wave due to the lack of field scan data. The fatigue life of a 120 m long span under a constant current-induced flow with the velocity of 0.7 m/s has been assessed. Results show that comparing with the fatigue life of a fixed span, the present method leads to an increase in the fatigue life by about ten times. Full article

Probabilistic random vibration can speed up wear and tear on several components of the tracked vehicle, including the track system, drivetrain, and suspension. Extended exposure to high levels of vibration can cause structural damage to the vehicle frame and other critical components. Assessing random vibration in track vehicles requires a comprehensive approach that considers both the root causes and potential consequences of the vibrations. This random vibration significantly influences the structural performance of suspension arm which is key component of tracked vehicle. Damage due to fatigue is conventionally computed using time domain loaded signals with stress or strain data. This approach generally holds good when loading is periodic in nature but not be a good choice when dynamic resonance is in process. In this case an alternative frequency domain fatigue life analysis is used where the random loads and responses are characterized using a concept called Power spectral density (PSD). The current research article investigates the fatigue damage characteristics of a tracked vehicle suspension arm considering the dynamic loads induced by traversing on smooth and rough terrain. The analysis focusses on assessing the damage and stress response Power spectral density (PSD) ground-based excitation which is termed PSD-G acceleration. Quasi Static Finite Element Method based approach is used to simulate the operational conditions experienced by the suspension arm. Through comprehensive numerical simulations, the fatigue damage accumulation patterns are examined, providing insights into the structure integrity and performance durability of the suspension arm under varying operational scenarios. The obtained stress response PSD data and fatigue damage showed that the rough terrain response exhibits higher stresses in suspension arm. The accumulated stresses in case of rough terrain may prompt to brittle failure at specific critical locations. This research contributes to the advancement to the design and optimization strategies for tracked vehicle components enhancing their reliability and longevity in demanding operational environments. Full article

The longevity of railway vehicles is an important factor in their mechanical and structural design. Fatigue is a major issue that affects the durability of railway components, and, therefore, knowledge of the fatigue resistance characteristics of critical components, such as leaf springs, must be extensively investigated. This research covers the fatigue resistance of 51CrV4 steel under bending and axial tension, for distinct stress ratios, in the low-cycle fatigue regime (LCF), high-cycle fatigue regime (HCF), and very high-cycle fatigue regime (VHCF) using experimental data collected in this work and from previous experiments. Two fatigue models were analyzed: the Walker model (WSN) and the Castillo–Fernández–Cantelli model, CFC, adapted for the presence of mean stress (ACFC). According to the analysis carried out, both fatigue resistance prediction models provided good results for the experimental data, with the ACFC model showing good fitting when considering all the failure data and outliers. Additionally, fracture surfaces showed a higher trend for crack initiation on the surface for positive stress ratios despite internal defects also possibly being responsible for some fatigue failures. This investigation aimed to provide a probabilistic fatigue model encompassing the LCF, HCF, and VHCF fatigue regimes for distinct stress ratios for the fatigue design analysis of 51CrV4 steel parabolic leaf springs manufactured by hot-forming processes with subsequent heat treatments. Full article

The development of innovative technologies and the employment of diverse material compositions have contributed to the enhancement of wear prediction methods. However, the accurate forecasting of service life and the identification of critical influencing factors remain challenging due to the complex interactions governing wear behaviour. Throughout history, various methodological approaches have been developed to model wear, primarily categorised into analytical calculations and experimental investigations. Analytical methods, including Archard’s equation and its variations, provide a theoretical basis for wear estimation. However, these models frequently depend on empirical coefficients derived from extensive experimentation, which restricts their predictive accuracy. Moreover, classical wear models do not fully account for material fatigue effects and 3D surface texture parameters, which are critical for solving complex engineering problems. Recent advancements have sought to address these limitations by integrating probabilistic surface modelling, fatigue-based degradation theories, and numerical simulations to enhance wear predictions. Experimental investigations remain essential for validating analytical models, as they provide empirical data necessary for parameter calibration. However, these experiments require specialised equipment and are often time-consuming and costly. The integration of modern measurement tools and numerical simulations, such as finite element analysis (FEA) and machine learning-based models, presents a promising direction for improving wear predictions. This review highlights the strengths and limitations of existing wear models and emphasises the need for further refinement of analytical approaches to incorporate fatigue wear mechanisms, real surface roughness effects, and environmental influences for more accurate and reliable wear assessments. Full article

The longevity of railway vehicles is an important factor in their mechanical and structural design. Fatigue is a major issue that affects the durability of railway components, and therefore, knowledge of the fatigue resistance characteristics of critical components, such as the leaf springs, must be extensively investigated. This research covers the fatigue resistance of chromium–vanadium alloy steel, usually designated as 51CrV4, from the high-cycle regime (HCF) (${10}^3$–${10}^4$) up to very high-cycle fatigue (VHCF) (${10}^9$) under the bending loading conditions typical of leaf springs and under uniaxial tension/compression loading, respectively, for a stress ratio, $R_{\sigma }$, of −1. Different test frequencies were considered (23, 150, and 20,000 Hz) despite the material not exhibiting a relatively significant frequency effect. In order to create a new fatigue prediction model, two prediction models, the Basquin SN linear regression model and the Castillo–Fernandez–Cantelli (CFC) model, were evaluated. According to the analysis carried out, the CFC model provided a better prediction of the fatigue failures than the SN model, especially when outlier failure data were considered. The investigation also examined the failure modes, observing multiple cracks for higher loads and single cracks that initiated on the surface or from internal inclusions at lower loading. The present investigation aims to provide a fatigue resistance prediction model encompassing the HCF and VHCF regions for the fatigue design of railway wagon leaf springs, or even for other components made of 51CrV4 with a tempered martensitic microstructure. Full article