Fatigue Data Research Articles

Evaluating the relationship between neurological function, neuromuscular fatigue, and subjective performance measures in professional rugby union players.

Managing the health and wellbeing of full-time professional athletes is a multifaceted task. In elite high-performance environments, medical staff and strength training coaches attempt to identify improved methods to monitor player health. Monitoring player health could indicate potential injury risk and assist in adjustments to training and workload management. Measuring fatigue is a notable component of monitoring player readiness before and after training sessions, and after competitive fixtures. In the present study, a novel method of gathering non-invasive player data was investigated by utilizing the Omegawave (OW) to monitor direct current (DC) potential brainwave activity. This method allowed for non-invasive data gathering to assess recovery, player readiness and indicators of workload that may affect optimal performance. DC potential is based on recording low electrical frequencies (>0.5 Hz) that is derived from (1) Stabilization point of DC potential (mV), (2) Stabilization time (1.0-7.0) and (3) Curve shape (1.0-7.0). These measures evaluate the athlete's internal stress, readiness to perform, and neurological function through DC potential brain wave activity and heart rate variability (HRV) assessments. The primary aim of this case series was to compare the efficacy of objective DC potential brainwave activity measurements (neurological function) with neuromuscular fatigue data using reactive strength index modified (RSImod) and profile of mood states (POMS) questionnaires to assess a player's state of readiness to train. The participants in this study were eleven male senior professional rugby union players with a mean stature (±SD) of 185.2 ± 8.6 cm, mass of 101.1 ± 12.9 kg, and age of 27.1 ± 2.1 years. All players were tested 3 days per week over a 6-week mid-season period. Results from this case study suggest that DC potentials could be used as an objective measure to indicate player readiness and managing individual player workload. The final analyses identified a weak negative correlation (r = -0.17) between the RSImod data and the DC potential data was observed. DC potential brainwave activity data could be used in conjunction with subjective measures such as POMS, RSImod and reported injury status to adjust player daily activity.

Diet and Disease Activity in Patients with Axial Spondyloarthritis: SpondyloArthritis and NUTrition Study (SANUT).

Axial Spondyloarthritis (axSpA) patients with inflamed intestines have higher SpA activity. Diets that modulate microbiota may influence inflammation and SpA activity. Today, data concerning the impact of diet on SpA activity are scarce. SANUT was a single-center, noninterventional, cohort study that assessed dietetic profiles associated with SpA activity in axSpA. Demographic, clinical, SpA-related, quality of life (QoL), fatigue, physical activity, and dietary data were collected. SpA activity was assessed by Ankylosing Spondylitis Disease Activity Score (ASDAS) and by Bath Ankylosing Spondylitis Disease Activity Index (BASDAI). We assessed whether high SpA activity was associated with nutriment consumption. Between 12 February 2018 and 12 February 2020, 278 patients participated. High SpA activity, as measured by ASDAS and BASDAI, was significantly associated with higher body mass index and waist circumference, negative HLA-B27, lower QoL, higher fatigue, and higher digestive-symptom scores. Furthermore, high SpA activity, as measured by BASDAI, was associated with female sex, smoking status, patients who were not actively employed, reduced physical activity, and high intake of ultra-transformed foods, while high SpA activity, as measured by ASDAS, was associated with low intake of omega-3 PUFAs and fiber. Therefore, low intakes of omega-3 PUFAs and fiber, and high intake of ultra-transformed foods, are associated with high SpA activity.

The next generation of fatigue prediction models: evaluating current trends in biomathematical modelling

Biomathematical models (BMMs) are parametric models that quantitatively predict fatigue and are routinely implemented in fatigue risk management systems in increasingly diverse workplaces. There have been consistent calls for an improved ‘next generation’ of BMMs that provide more accurate and targeted predictions of human fatigue. This article examines the core characteristics of next-generation advancements in BMMs, including tailoring with field data, individual-level parameter tuning and real-time fatigue prediction, extensions to account for additional factors that influence fatigue, and emerging nonparametric methodologies that may augment or provide alternatives to BMMs. Examination of past literature and quantitative examples suggests that there are notable challenges to advancing BMMs beyond their current applications. Adoption of multi-model frameworks, including quantitative joint modelling and machine-learning, was identified as crucial to next-generation models. We close with general recommendations for researchers, practitioners, and model developers, including focusing research efforts on understanding the cognitive dynamics underpinning fatigue-related vigilance decrements, applying emerging dynamic modelling methods to fatigue data from field settings, and improving the adoption of open scientific practices in fatigue research.

Label-Less Learning for Urban Railway Transit Driver Fatigue Detection with Heart Rate Variability

Driver fatigue has a direct impact on urban railway transit (URT) drivers’ driving behavior and can cause driver error. The existing methods for fatigue detection mainly train the models with supervised learning, relying heavily on the annotation of recorded data. However, labeled data are unobtainable in some environments, especially for URT driver fatigue levels during actual driving. Therefore, this study proposes a fatigue detection method using unlabeled heart rate variability data to monitor URT driver fatigue in actual working conditions. By utilizing the existing conclusions with regard to factors contributing to fatigue and physiological changes, this study annotated a small number of samples and then used a novel positive and unlabeled learning algorithm based on nearest neighbors and random forest to divide samples into different fatigue levels. The proposed method was evaluated using the URT driver fatigue data sets collected in the lab. Binary classification achieved an accuracy of 79.0%. However, the accuracy of three-class classification was only 55.7%. In addition, the proposed method performed as well using the field data set as it did using the lab data set. The results show the high generalization performance of the proposed method, which could contribute to addressing the issue of lack of labeled training data for fatigue detection in actual working conditions.

Statistical distribution of gear surface fatigue lives at high reliability

The reliability of gears is important for a wide variety of applications, and the gearing community has a desire for better prediction of fatigue life at very high reliability. To improve predictions, a set of 950 NASA-conducted gear surface fatigue test results were gathered, pooled together using the general standardized Weibull variate, and then fit to a Weibull three-parameter distribution. An iterative fitting method was developed and then applied to the gear fatigue data to determine the values of the distribution parameters. Additional analyses were done using bearing and gear data that demonstrate a trend of increasing minimum life coefficient as correlated to the timeframe of the fatigue testing, and material cleanliness is proposed as a primary explanation for such correlation. It is noted that this observed trend supports published findings from analytical modeling and experimental studies that improvements to material cleanliness improves minimum fatigue life. Further use and study of the three-parameter Weibull distribution is recommended as a tool to quantify fatigue life most accurately in the high reliability regime.

Influence of artificial seawater on the VHCF behavior of different steels

AbstractThe fatigue properties of a high‐strength steel, a structural steel, and a duplex stainless steel in artificial seawater were investigated. Consequently, a test chamber for the existing ultrasonic test stand was implemented. The experiments were performed with smooth specimens at a loading with R = −1 up to 2 · 109 cycles. The specimens were fully submerged in artificial seawater. The fatigue data were statistically evaluated using the pearl string method and compared to different SN curves from the literature in air. The results show continuously decreasing SN curves under seawater conditions for all materials, with no detectable fatigue limit. Fracture surface investigations reveal large corrosion areas for the structural steel with, generally, no clear crack initiation location. The duplex stainless steel shows less corrosion pits but a high degree of scatter of the SN data. For the high‐strength steel, multiple crack initiation locations were observed from the surface.

Robust quantile regression analysis for probabilistic modelling of S-N curves

The scatter in fatigue data is commonly characterized by probability distributions for constructing the probabilistic S-N curves. However, there is notable estimation bias under distribution misspecification. In this paper, we proposed a quantile regression framework for modeling S-N curves. The quantile regression model can be built directly on the experimental data without any distribution assumption. Extensive simulations and two experimental datasets are used to illustrate the usefulness of the proposed model. The results demonstrate that the quantile regression model is exempt from the problem of incorrectly specifying the potential fatigue life distribution and is robust to the non-constant scale problem.

Challenge of Establishing Materials Testing Standards with Miniature Specimen for Solders

Abstract Solder is an essential material for connecting parts inside electrical devices. Although there is an international standard for the chemical composition of solder, there is no testing standard for evaluating the mechanical properties and characteristics of solder as a structural material. The Committee on High Temperature Strength of Materials, the Society of Materials Science, Japan, proposed some testing standards for solders in the early 2000s based on the results from research activities of the Solder Strength Evaluation Method Working Group. The primary objective of the standardization at that time was to acquire stable testing data and to eliminate institutional differences. The recommended specimen diameter is 10 mm. To verify the size difference between the 10-mm-diameter specimen and an actual solder joint inside an electrical device, the working group has been investigating materials testing techniques using a specimen with a reduced diameter of 3 mm. The research results were published in 2020 as Standard for Miniature Testing of Solders. We have also conducted a series of systematic experiments based on the testing standards for Sn-3.0Ag-0.5Cu. The results obtained so far are as follows: In the tensile tests, the tensile strength of the 10-mm- and 3-mm-diameter specimens are almost identical, independent of the specimen size, although the casting method and heat treatment conditions are different. The effect of specimen size on the creep rupture life is insignificant. In the low-cycle fatigue tests, stable fatigue data can be obtained even at high temperatures using the 3-mm-diameter specimen.

Effects of physical stress in alpine skiing on psychological, physiological, and biomechanical parameters: An individual approach.

Alpine skiing is an attractive winter sport that often includes mental and physical demands. Since skiing is often done for several hours, fatigue processes occur that might lead to action errors associated with a higher risk of accidents and injuries. The aim of this study was to investigate the timing of changes in subjective, physiological, and biomechanical parameters during a physically demanding, standardized, non-competitive alpine skiing session. A group of 22 experienced male skiers carried out 10 runs, each lasting between 150 and 180 s, at a turn rate of 80 turns per minute with their best skiing technique. Immediately after the run, skiers reported ratings of fatigue, and other affective states. During skiing, breathing pattern and biomechanical data of the ski turns as radial force, turn duration, edge angle symmetry, and a composed motion quality score were recorded. Analyses of variances on skiers showing signs of fatigue (n =16) revealed that only the subjective data changed significantly over time: fatigue and worry increased, vitality and calm decreased. Subsequently, individual change points analyses were computed to localize abrupt distribution or statistical changes in time series data. For some skiers, abrupt changes at certain runs in physiological and/or biomechanical parameters were observed in addition to subjective data. The results show general effects in subjective data, and individual fatigue-related patterns concerning the onset of changes in subjective, physiological, and biomechanical parameters. Individuality of response to fatigue should be considered when studying indicators of fatigue data. Based on the general effects in subjective data, it is concluded that focusing on self-regulation and self-awareness may play a key role, as subjective variables have been shown generally sensitive to the physical stress in alpine skiing. In the future, customized algorithms that indicate the onset of fatigue could be developed to improve alpine skiers' self-awareness and self-regulation, potentially leading to fewer action errors.

Influence analysis of different compaction degrees on the fatigue performance for stranded copper power conductors

ABSTRACT The influence of compaction degrees of 120 mm2 stranded copper conductors on their fatigue performance was investigated. Conductors with two different compaction degrees were fatigue tested in tension-tension loading and the fatigue data for the two sets of conductors seemed not to fell within the same scatter band. Wires of stranded copper conductors were deformed due to the compaction procedure applied during fabrication. The fatigue data were adjusted considering the stress concentrations due to the deformation and the adjusted data fell within the same scatter band. Then the influence analysis of compaction degrees was performed. The compaction procedure was simulated by using finite element method and the results were compared with real conductors. A simplified fitting formula was proposed to describe the quantitative relationship between fatigue life and compaction degrees of copper conductors and a predicted fatigue life table was concluded which is helpful for cable design.

Bayesian optimized collection strategies for fatigue strength testing

AbstractA statistical framework is presented enabling optimal sampling and analysis of constant life fatigue data. Protocols using Bayesian maximum entropy sampling are built based on conventional staircase and stress step methods, reducing the requirement of prior knowledge for data collection. The Bayesian Staircase method shows improved parameter estimation efficiency, and the Bayesian Stress Step method shows equal accuracy to the standard method at larger step size allowing experimentalists to lessen concerns of loading history. Statistical methods for determining model suitability are shown, highlighting the influence of protocol. Experimental validation is performed, showing the applicability of the methods in laboratory testing.

Corrosion Fatigue Test and Performance Evaluation of High-Strength Steel Wires Based on the Suspender of a 11-Year-Old Concrete-Filled Steel Tube Arch Bridge

In this study, based on an actual project, the steel wire in the suspender of a concrete-filled steel tube arch bridge was selected from appropriate samples to perform acetic acid-accelerated salt spray tests, and steel wire samples with different corrosion degrees were obtained. A laser microscope was used to measure the roughness of the samples, and three corrosion samples were randomly selected from each group for grinding. Fatigue tests were performed on steel wire samples with different degrees of corrosion and polished steel wire samples, and the fatigue fracture was observed and analyzed. The results showed that few cracks on the steel wire surface could be eliminated by grinding, and the fatigue life of the specimens could be increased. Finally, the stress-life-mass loss rate (S-N-η) probability model based on the Weibull distribution was obtained using the measured fatigue data, which can be further referenced in the planning of the inspection of suspender service status, steel wire corrosion protection, and fatigue life prediction, etc.

Unified fatigue characterization of asphalt mixture under multi-field coupling condition: Stress state, frequency, and temperature

For a scientific and reliable design of asphalt mixture subjected to fatigue damage, a comprehensive understanding and realistic prediction of asphalt mixture fatigue life under complex loading conditions with variable loading rates or temperature were a major concern. In this study, direct tensile test, uniaxial compression test, indirect tensile test, and four-point bending test were used to study the fatigue behavior of asphalt mixtures with a particular focus on the action of temperature and frequency. Strength and fatigue were investigated separately under varied loading rates at several temperatures. A new idea for determining the stress ratio based on the strength failure envelope line considering temperature and loading rate was proposed. The connection between strength and fatigue was established by the stress ratio. The test conditions for strength and fatigue tests were set to be the same leading to the fatigue data distributed regularly. Experimental data were analyzed and normalized by studying the evolution of the number of cycles with the stress ratio. A new analytical approach was introduced to simulate the fatigue performance. The fatigue performance under multi-field coupling conditions was uniformly characterized. The dispersion of fatigue equation caused by the test method and conditions is eliminated. Engineering application showed that the design deviation of the pavement fatigue resistance was reduced to less than 6%.

Long-Term Neurological Sequelae Among Severe COVID-19 Patients: A Systematic Review and Meta-Analysis.

Few studies have thoroughly evaluated the neuro-invasive effect of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)infection, which may contribute to a wide range of sequelae from mild long-term effects like headaches and fatigue to severe events like stroke and arrhythmias. Our study aimed to evaluate the long-term neurological effects of coronavirus disease 2019 (COVID-19) amongpatients discharged from the hospital. In this systematic review and meta-analysis, we assessed the long-term neurocognitive effects of COVID-19. Post-COVID-19 neurological sequelae were defined as persistent symptoms of headache, fatigue, myalgia, anosmia, dysgeusia, sleep disturbance, issues with concentration, post-traumatic stress disorder (PTSD), suicidality, and depression long after the acute phase of COVID-19. Data from observational studies describing post-COVID-19 neurocognitive sequelae and severity of COVID-19 from September 1, 2019, to the present were extracted following the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol with a consensus of three independent reviewers. A systematic review was performed for qualitative evaluation and a meta-analysis was performed for quantitative analysis by calculating log odds of COVID-19 neurocognitive sequelae. The odds ratio (OR) and 95% confidence interval (CI) were obtained and forest plots were created using random effects models. We found seven studies, out of which three were used for quantitative synthesis of evidence. Of the 3,304 post-COVID-19 patients identified, 50.27% were male with a mean age of 56 years; 20.20%had post-COVID-19 symptoms more than two weeks after the acute phase of infection. Amongpersistence symptoms, neurocognitive symptoms like headache (27.8%), fatigue (26.7%), myalgia (23.14%), anosmia (22.8%), dysgeusia (12.1%), sleep disturbance (63.1%), confusion (32.6%), difficulty to concentrate (22%), and psychiatric symptoms like PTSD (31%), feeling depressed (20%), and suicidality (2%) had a higher prevalence. In meta-analysis, COVID-19 patients with severe symptoms had higher odds of headache (pooled OR: 4.53; 95% CI: 2.37-8.65;p<0.00001; I2: 0%) and myalgia (pooled OR: 3.36; 95% CI: 2.71-4.17;p<0.00001; I2: 0%). Anosmia, fatigue, and dysgeusia had higher but non-significant odds following COVID-19. Although we had sufficient data for headache and fatigue to identify higher rates and associations following COVID-19, we could not establish relationships with other post-COVID-19 neurocognitive séqueles. Long-term follow-up may mitigate the neurocognitive effects amongCOVID-19 patients as these symptoms are also associated with a poor quality of life.

Fatigue life prediction of a L-PBF component in Ti-6Al-4V using sample data, FE-based simulations and machine learning

Laser Powder Bed Fusion (L-PBF) is a widely-used additive manufacturing (AM) technique for producing complex metal parts used for a variety of dynamically loaded applications. Fatigue performance of standardized L-PBF samples is at present fairly well understood, while the knowledge on the fatigue behaviour of real-life complex shaped components is often lacking. This work presents insight, methods and results on predicting L-PBF component fatigue life using FE-based simulations, stress-based sample fatigue data and machine learning respectively. A realistic end-use part with representative geometry for many industrial applications was selected and produced in Ti-6Al-4V by L-PBF along with many standardized samples under different building orientations and with different types of heat treatments and surface finishing steps. The results indicate that the developed tool for component fatigue life prediction can accurately predict both the failure location and the number of cycles to failure.

Uniaxial and multiaxial fatigue behaviour of wire arc additively manufactured ER70S-6 low carbon steel components

Wire arc additive manufacturing (WAAM), also known as directed energy deposition (DED) process, is an efficient additive manufacturing technology, offers high potential to rapidly fabricate large-scale parts with complex geometries layer-by-layer. However, the fundamental understanding of the fatigue behaviour of such parts and the material requirements need to be significantly improved at all levels before this unique technology can be implemented for critical applications. This work aims to investigate the fatigue behaviour of WAAM built ER70S-6 steel under uniaxial, torsion and multiaxial loading conditions. Specimens were extracted in two different orientations: vertical and horizontal, to explore if the orientation direction has any effect on the fatigue results. Scanning Electron Microscopy (SEM) was conducted to examine the fracture surface of broken specimens and identify crack initiation regions and fracture mechanisms. The obtained results were compared with the fatigue data available in the literature on common structural steels fabricated using conventional welding and WAAM technique, showing similar fatigue behaviour with wrought S355 specimens. Moreover, the uniaxial data set on ER70S-6 WAAM specimens was evaluated according to the DNV RP-C203 standard for continuous welds, demonstrating advantageous fatigue resistance in the examined material.

On the Microstructure, Residual Stress and Fatigue Performance of Laser Metal Deposited TC17 Alloy Subjected to Laser Shock Peening.

Laser shock peening (LSP) has been employed to improve the mechanical properties of repaired aerospace engine components via laser metal deposition (LMD). This study looked at cross-sectional residual stress, microstructure and high cyclic fatigue performance. The outcomes demonstrated that a compressive residual stress layer with a value of 240 MPa was formed at a depth of 200 μm in the laser melting deposited zone and the microhardness was improved by 13.1%. The findings of electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM) analysis revealed that misorientation increased and dislocation features were observed after LSP which is beneficial to the enhancement of fatigue performance. The high cycle fatigue data illustrated that the LMD+LSPned samples exhibited 61% improvement in comparison to the as-LMD samples. In the aerospace sector, LSP and LMD are therefore very effective and promising techniques for restoring high-value components.

On the Transferability of Fatigue and Cyclic Deformation Data to 100 µm Thin Structures

The fatigue properties and cyclic deformation behaviour of materials are usually determined using cylindrical specimens (e.g., Ø7.5 mm). Since the transferability to very small dimensions has not been comprehensively considered so far, this study investigates the transferability of specimen data from high-strength steel X5CrNiCuNb16-4 to real structures characterised by a wall thickness of 100 µm. Regarding fatigue, extensive calculations demonstrate that for notched specimens and thin structures, both the material-mechanical support factor concept according to the FKM guideline and the point method of the Theory of Critical Distance (TCD) make correct predictions of the local fatigue strength, with a maximum deviation from experimental values of less than 5%. However, the study points out that the TCD is only conditionally applicable for thin-walled structures, as the material parameter a0 must be significantly smaller than the wall thickness. Regarding the deformation behaviour, the material reveals special flow characteristics in the first hysteresis. Nevertheless, a combined hardening approach is suitable for modelling. The validation of the model by a plastic deformation of the structure seems plausible, although geometric influences prove to be dominant. In conclusion, even 100 µm thin structures can be evaluated using conventional specimen tests and established assessment or modelling methods.

Novel Fatigue Testing of Extruded Inconel 718

This paper presents accelerated fatigue testing results of extruded Inconel 718. Tapered cantilever specimens, that is designed to achieve uniform maximum bending stresses along their gage lengths, were used for high-speed fatigue testing in rotate-bending mode. Random fracture proved the uniformity of bending stress along the gage length. The endurance limits and fatigue lives of Inconel 718 were significantly impacted by material hardness and surface finish. The aged and polished specimens can achieve about one order of magnitude longer in fatigue lives compared to the unaged and as-machined specimens. Optical and scanning electron microscopic examinations showed the microstructure and mechanism of fatigue failure. Brittle particles, shattered during fatigue test, formed internal cracks and voids that linked and fractured the remaining matrix in ductile mode; circumferential cracks would initiate at deep machining marks on the specimen surface, propagate inward until reaching a critical crack length and then fracture catastrophically. A polished specimen, having no machining mark and smooth surface finish, exhibited multiple and random crack origins that propagated simultaneously inward till fracture. The resulted fatigue data were compatible with other published data for the wrought Inconel 718.

Corrosion fatigue investigations on cast, forged and welded nickel-martensitic steels for hydro turbines

Facing an increased demand for flexible operation of hydro power plants (HPPs), Francis runners are substantially subjected to operating conditions beyond the traditional operating ranges. In order to ensure a reliable service of major HPP components covering their complete service life, a fundamental understanding of both load and material fatigue behavior is essential. Due to the cyclic loading in water, reliable corrosion fatigue data is required for the runner fatigue assessments. In previous publications, a state-of-the-art corrosion fatigue testing campaign within a collaborative German research project was described and results for a common cast steel used for the manufacturing of hydro runners were presented. The material testing program for the 13 % Chromium and 4 % Nickel steel has advanced since then and important aspects beyond varying water qualities and different stress ratios have been investigated. Within this conference contribution, corrosion fatigue investigations that were carried out on cast, forged and welded nickel-martensitic steels are described and recently gained results are presented.