Load Components Research Articles

Engineering method for quantifying the coupling effect of transmission tower-line system under strong winds

For transmission tower-line (TL) systems, the coupling effect between line cables and towers under strong winds is significant. This paper presents a method to quantify the coupling effect. Assuming that effective separation of line cables and towers is attainable, this work transforms the coupling effect into the transferred load from the line cable to the target tower, the coupling participation mass of the line cable, and the additional damping. The effective separation conditions are defined through an optimization method minimizing the residual errors of the wind-induced response and dynamic characteristics between the separated bodies and the TL system. A typical TL system is considered and analyzed for its structural dynamic characteristics and wind-induced response. Particularly, the quantities associated with the coupling effect of the TL system are estimated. It reveals that the transferred dynamic load component parallel to the line cable which is overlooked in current codes is significant and highly sensitive to the separation boundary conditions of line cables. Furthermore, the coupling participation mass of the conductor is more prominent than that of the ground wire. The proposed method is feasible for quantifying the TL coupling effect and incorporating it into the wind-induced response analysis of transmission line structures.

A Quasi Time-Domain Method for Fatigue Analysis of Reactor Pressure Vessels in Floating Nuclear Power Plants in Marine Environments

The reactor pressure vessel (RPV) in onshore nuclear power plants is typically analysed for fatigue life by considering the temperature, internal pressure, and seismic effects using a simplified time-domain fatigue analysis. In contrast, the frequency-domain fatigue analysis method is commonly employed to assess the fatigue life of ship structures. The RPV of a floating nuclear power plant (FNPP) is subjected to a combination of temperature, internal pressure, and wave loads in the marine environment. Consequently, it is essential to effectively integrate the frequency-domain fatigue analysis method used for hull structures with the time-domain fatigue analysis method for RPVs in FNPPs or, alternatively, to develop a suitable method that effectively accounts for the temperature, internal pressure, and wave loads. In this study, a quasi-time-domain method is proposed for the fatigue analysis of RPVs in FNPPs. In this method, secondary components of marine environmental loads are filtered out using principal component analysis. Subsequently, the stress spectrum induced by waves is transformed into a stress time history. Fatigue stress under the combined influence of temperature, internal pressure, and wave loads is then obtained through a stress component superposition method. Finally, the accuracy of the quasi-time-domain method was validated through three numerical examples. The results indicate that the calculated values obtained by the quasi-time-domain method are slightly higher than those obtained by the traditional time-domain method, with a maximum deviation of no more than 24%. Additionally, the computation time of the quasi-time-domain method is reduced by 98.67% compared to the traditional time-domain method.

Plane Wave Finite Element Method for the Dynamic Response of the Periodic Pile-Type Composite Ground Under a Harmonic Concentrated Load

In this study, a simplified model for the pile-type composite ground, namely, the periodic pile-type composite ground (PPCG) model is proposed. The PPCG in this study is assumed to be periodic in the horizontal directions and finite in the vertical direction. For investigation of the dynamic response of the PPCG to a harmonic concentrated load, a plane wave finite element (PWFE) method for the PPCG under the load is developed in this study. To develop the method, the concentrated load is decomposed into its wavenumber domain components by the 2D Fourier transform first. The response of the PPCG to a wavenumber domain load component is represented by a set of plane waves, and the variables associated with each plane wave are discretized via the FEM along the vertical direction of the PPCG. The finite element equations for each plane wave of the PPCG are developed via the virtual work principle. To obtain the overall dynamic response of the PPCG to the concentrated load by synthesizing the responses due to all load components effectively, the mode superposition method is used to construct the response of the PPCG to the load component, and the polar coordinate system-based inverse Fourier transform method is developed for the inversion of the 2D Fourier transform with respect to the wavenumber. With the developed method, some numerical results about the dynamic response of the PPCG to the concentrated load are presented.

To Hear or Not to Hear: Cognitive Load Theory and Learning

ABSTRACT Background: Cognitive load is the amount of information one’s working memory can process at any given time. Key components of cognitive load include germane load, intrinsic load, and extraneous load. Learning is hindered if overload occurs, thus reducing cognitive performance. This study addresses the impact of auditory stimulation on cognitive load through repeated recognition tasks. Methods: Twenty-eight Queen’s University students were recruited and randomly allocated to control and experimental groups who completed the experiment with noise-cancelling headphones and with ambient noise, respectively. The experiment consisted of learning and testing phases. The learning phase involved memorizing a series of words. Subsequently, the testing phase required participants to recall words from the list. Each trial consisted of one learning and one testing phase, with three trials per participant. Cardiovascular measurements were taken prior to the experiment, at the beginning of each phase, and after each trial was completed. Results: Primary outcomes demonstrated that the experimental group experienced greater percent changes in heart rate and rate pressure product, higher perceived mental effort, and greater within-group variation for heart rate compared to controls. Scores decreased as trials progressed. Secondary outcomes showed that the top 33% of participants were less susceptible to changes in heart rate and blood pressure. Overall, four heart rate or blood pressure responders were identified. Conclusions: Exposure to 70 decibels of ambient noise contributes to cognitive load as noted by elevated cardiovascular measure and perceived mental effort. Results can inform educational platforms and hospital logistics.

Numerical study of internal solitary wave loads on a submerged slender body with multi-parameter coupling

This study investigates the destabilizing loads exerted on submarines by large-amplitude internal solitary waves (ISWs), which significantly increase the risk of a phenomenon known as “falling deep.” Using numerical simulations and theoretical analysis, the research explores the multi-parameter coupling effects of ISWs on a slender body in a two-layer fluid system. A numerical wave generation method for large-amplitude ISWs, based on the strongly nonlinear adjusted high-order unidirectional (aHOU) model, is proposed. A corresponding numerical model is also developed to simulate the interaction between ISWs and a submerged slender body, with validation against experimental data confirming its accuracy and reliability. The study further examines how wave amplitude, submergence depth, and wave incidence angle affect the load characteristics induced by ISWs. Theoretical analysis identifies the components of ISW-induced loads, revealing a linear relationship between horizontal load and wave amplitude, as well as the influence of submergence depth on the duration of vertical forces. The primary contributor to the horizontal force is identified as the pressure-gradient force generated by the ISW's flow field, while the vertical force is primarily driven by the reduced gravity force due to density stratification and wave forces, which are well-approximated by Morison's formula. Additionally, the peak values of horizontal and vertical forces are significantly affected by wave incidence angle and wave amplitude, respectively. These findings provide a theoretical foundation for understanding the “falling deep” phenomenon encountered by submarines under the influence of ISWs.

Electrochemical synthesized copper mesh catalysts for catalytic combustion: A comprehensive study of Mn doping and atmospheric conditions during calcination

Metal-based monolithic catalysts offer significant advantages in heterogeneous catalysis attributed to their exceptional thermal conductivity and mechanical strength. However, controlling the loading of active components on metallic substrates remains a major challenge. Here, we synthesized a Mn-doped Cu mesh catalyst using a facile electrochemical method, allowing the active components to grow directly on Cu mesh without extra pretreatments or binding agents. The synthesized catalyst exhibits approximately a 30 % increase in carbon monoxide oxidation activity at 100°C and a 60 % increase in toluene oxidation activity at 240°C compared to the Mn-free Cu mesh catalyst. We found that calcination in inert gases enhances interactions between Cu and Mn species, leading to more oxygen vacancies and adsorption sites for reactants. This investigation paves the way for fabricating multi-metal monolithic catalysts on metallic substrates.

One-step development of photothermal omniphobic membrane for vacuum membrane distillation towards sustainable water desalination using solar energy

Nowadays, development of photothermal omniphobic membranes (POMs) for water desalination using solar energy has gained considerable attention to face water and energy challenges in a sustainable way. Scientists have been developing POM via different techniques based on photothermal component loading and surface energy reduction, all of which suffer from multi-step processes. Obviously, researchers and especially industrial developers aspire to one-step techniques that make the production process highly accelerated and economical. In this study, a one-step technique to develop POMs is demonstrate, using which the omniphobic and photothermal properties are simultaneously imparted to the membrane through being integrated with the membrane fabrication process. The technique works based on elaborate design of membrane surface chemistry and roughness via electrospinning of poly(vinylidene fluoride) (PVDF)/poly(1H,1H,2H,2H-perfluorooctyl acrylate) (PPFOA) blend solution containing carbon black nanoparticles (CB NPs). Optimum concentrations for PPFOA and CB NPs with respect to the PVDF weight were obtained as 60 and 15 wt%, respectively. The developed membrane exhibited permeate flux and salt rejection as high as 2.94 kg/m2·h and > 98 %, respectively, under one sun radiation. Evaporation rate and efficiency of the developed membrane were calculated as 1.49 kg/m2.h and 83.5 %, respectively. The membrane exhibited stable long-term performance for 600 min in contact with the sodium dodecyl sulfate (0.8 mM)-containing NaCl aqueous solution (3.5 wt%). Besides single-step implementation, the introduced technique benefits from endowing the entire membrane structure with omniphobic and photothermal properties not only the membrane surface. This can drastically improve the membrane performance and life time.

Reliability analysis of load sharing systems under unequal load-sharing rule with applications

We are examining a setup consisting of several parallel-connected k-out-of-m load sharing systems. The load sharing is modeled using proportional conditional failure rates for component failures and load sharing under the unequal load-sharing rule. We have derived the system reliability for the entire setup. To illustrate this, we examine two cases: one with n systems connected in parallel, each following a 1-out-of-2 configuration, and the other with n systems connected in parallel, each following a 2-out-of-4 configuration. The system components are assumed to be identical with an exponential as well as Weibull baseline distributions. We propose two methods to estimate the baseline parameter and load-sharing parameters. Additionally, we present a confidence interval using bootstrapping techniques, specifically the percentile bootstrap (boot-p). We assess the performance of these parameter estimation and interval estimation methods through simulation studies. Furthermore, we evaluate the practical applicability of the proposed techniques by analyzing two real datasets. The same approach can be applied to a system that includes multiple k-out-of-m load sharing systems, where these systems are connected in either a series or a p-out-of-q configuration.

How reliable is the Real Adsorbed Solution Theory (RAST) for estimating ternary mixture equilibrium in microporous host materials?

Microporous crystalline adsorbents such as zeolites, and metal-organic frameworks (MOFs) have potential use in a wide variety of separations applications. In applications such as CO2 capture, the Ideal Adsorbed Solution Theory (IAST) often fails to provide a quantitative description of mixture adsorption equilibrium especially in cation-exchanged zeolites. The failure of the IAST is ascribable to non-compliance with one or more tenets mandated by the IAST such as (a) homogeneous distribution of adsorbates within the pore landscape, (b) no preferential location of guest species, and (c) absence of molecular clustering due to say hydrogen bonding. The focus of this article is on the reliability of the Real Adsorbed Solution Theory (RAST) models for quantitative estimation of adsorption equilibrium. Configurational-Bias Monte Carlo (CBMC) simulations are undertaken to determine the adsorption equilibrium for ternary CO2/CH4/N2, CO2/CH4/C3H8, CO2/CH4/H2, and water/methanol/ethanol mixtures in NaX, LTA-4A, CHA, DDR, and MFI zeolites. Additionally, CBMC simulations of the constituent binary pairs are used to determine the Wilson or NRTL parameters, taking due account of the dependence of the activity coefficients on the spreading pressure. Use of the binary pair Wilson or NRTL parameters allows the estimation of ternary mixture adsorption equilibrium, that is tested against the CBMC data on component loadings. In all investigated guest/host combinations, the RAST provides a good estimation of ternary mixture adsorption equilibrium.

Co-Delivery of VEGF siRNA and THPP via Metal-Organic Framework Reverses Cisplatin-Resistant Non-Small Cell Lung Cancer and Inhibits Metastasis through a MUC4 Regulating Mechanism.

Cisplatin resistance significantly impacts the antitumor efficacy of cisplatin chemotherapy and contributes to poor prognosis, including metastasis. In this study, we present the utilization of metal-organic framework (MOF) nanoparticles as the therapeutic component and drug loading scaffold for implementing a ternary combination therapeutic strategy to combat cisplatin-resistant lung cancer and metastasis. Specifically, by engineering MOFs (Cis@MOF-siVEGF) through the self-assembly of THPP as photosensitizer for photodynamic therapy (PDT), along with the incorporation of cisplatin (DDP) and VEGF siRNA (siVEGF), we propose the leverage of photodynamic-induced oxidative damage and gene silencing of the angiogenic factor to reverse cisplatin resistance and sensitize therapeutic potency. Our findings demonstrated that the chemo/photodynamic/antiangiogenic triple combination therapy via Cis@MOF-siVEGF under irradiation effectively inhibits cisplatin-resistant tumor growth and induces abscopal effects. Importantly, molecular mechanistic exploration suggested that MUC4 exerted regulatory effects on governing cancer metastasis, thus representing a potential immunotherapeutic target for cancer intervention. Overall, our study creates a MOFs-based multicomponent delivery platform for complementary therapeutic modules with synergistically enhanced antitumor efficacy and sheds light on potential regulatory mechanisms on cisplatin-resistance cancers.

Advancing Economical and Environmentally Conscious Electrification: A Comprehensive Framework for Microgrid Design in Off‐Grid Regions

AbstractThe design of renewable energy systems traditionally emphasizes life cycle costs, often focusing primarily on emissions rather than a comprehensive life cycle impact assessment. This research proposes a four‐tier methodology to balance cost‐effectiveness and sustainability in the electrification of remote areas. Tier 1 focuses on understanding the community context by analyzing electrical load profiles, meteorological data, and component specifications for microgrid design. Tier 2 evaluates the feasibility of various systems, optimizing them through cost analysis and Multi‐Criteria Decision‐Making (MCDM) to rank alternatives. Tier 3 assesses environmental impacts using life cycle assessment, ranking alternatives based on environmental criteria. Tier 4 integrates cost and environmental rankings to determine the most suitable energy configurations, followed by sensitivity analysis to ensure robust decision‐making. The methodology is validated through a case study of an unelectrified remote community, demonstrating that the PV‐Wind Turbine‐Biomass Generator‐Converter configuration is the most robust alternative, proving to be the optimal choice in 50% of the analyzed scenarios, achieving a Cost of Energy of 0.213 USD/kWh while minimizing environmental impact across all 18 criteria considered over a 25‐year life cycle. This novel framework offers a scalable approach to designing renewable energy systems, enhancing sustainable electrification efforts in developing regions.

Small Molecule Hydrogels Loading Small Molecule Drugs from Chinese Medicine for the Enhanced Treatment of Traumatic Brain Injury.

Self-assembly of hydrogels for mechanical support and drug delivery has been extensively researched in traumatic brain injury (TBI), where treatment options are limited. The chief challenge is that most self-assembled hydrogels rely on high molecular carriers or the incorporation of exogenous inactive substances as mediators. It is difficult for these drug delivery systems to achieve clinical translation due to concerns regarding biological safety. Here we report a small molecule hydrogel (GBR-gel) loading small molecule drugs (glycyrrhizic acid, berberine, and rhein) that originated from popular Chinese medicines without additional drug loading or inactive components under physiological conditions. In the long run, GBR-gel possesses several advantages, including ease of preparation, cost-effectiveness, and high biocompatibility. As a proof-of-concept, GBR-gel allows for prompt administration at the site of brain injury to exert potent pharmacodynamic effects. Further single-cell RNA sequencing and experimental validation indicated that GBR-gel can effectively rescue the suppressed glutamatergic synapse pathway after TBI, thereby attenuating inflammatory responses and neural impairments. Our work provides an alternative strategy for timely intervention of TBI.

Mapping dominant tree species of miombo woodlands in Western Tanzania using PlanetScope imagery

Mapping dominant tree species in miombo woodlands is essential for enhancing their monitoring and management. We evaluated PlanetScope imagery to map Julbernardia globiflora, Brachystegia spiciformis, and Pterocarpus tinctorius in Tongwe Forest Reserve, Tanzania. The study assessed the effectiveness of PlanetScope bands in discriminating tree species and investigated how different months/seasons influenced tree species classification. Optimal months (seasons) and spectral bands were selected using Principal Component loading, temporal pattern analysis, mean decrease in accuracy, and mean decrease Gini techniques. Random forest classification was employed for tree species classification, and accuracy was assessed using an error matrix. The study identified March, July, and September as optimal months for acquiring imagery, with effective bands including blue, green-1, green, yellow, red, and red-edge. July and September imagery in the dry season achieved higher overall accuracies of 65% and 69%, respectively, while March imagery in the wet season reached 55%. The highest overall accuracy of 72% was achieved using images from different seasons. Producer’s accuracy was highest for Brachystegia spiciformis (79%) and Julbernardia globiflora (95%), whereas Pterocarpus tinctorius had lower accuracy (25%). User’s accuracy varied with 74% for Brachystegia spiciformis, 70% for Julbernardia globiflora, and 67% for Pterocarpus tinctorius. Mapping accuracy was notably high for Brachystegia spiciformis and Julbernardia globiflora, reflecting their high sample size (dominance) and distinct phenology. The yellow and red bands were particularly effective in distinguishing miombo tree species demonstrating PlanetScope’s capability. Future research should focus on scaling up PlanetScope’s application for broad miombo tree species mapping.

Key performance indicators and reference values for turn performance in elite youth, junior and adult swimmers

ABSTRACT This study aimed to determine kinematic and kinetic key performance indicators (KPI) of swimming turn performance using principal component analysis (PCA) and multiple linear regression analysis and provide reference values using percentiles. Touch and tumble turn performances of male (n = 68) and female (n = 48) Swiss national team members from three age categories—adult (20.2 ± 2.7 yrs, 790 ± 57 points), junior (16.2 ± 0.8 yrs, 729 ± 53 points) and youth swimmers (14.4 ± 1.0 years of age, 667 ± 53 World Aquatics swimming points, respectively)—were assessed with a motion analysis system equipped with a force plate on the pool wall, one over- and four underwater cameras sampling forces at 500 Hz and footages at 100 Hz. The PCA reduced the 27 original variables by up to 15% depending on turn type and age category using Varimax component loading of >0.6 and explained up to 91% of the total variance. The highest Varimax component loadings for each principal component were used to determine KPI for each turn type and age category using multiple-regression analysis with total turn time as dependent variable. These KPI should be used to interpret turn performances and identify individual swimmers’ strengths, weaknesses and future potentials with the help of the percentiles as reference values.

Real-Time estimation of internal and solar heat gains in buildings using deep learning

This paper presents real-time monitoring of dynamic internal and solar heat gains using programmable low-cost cameras and deep learning techniques. For monitoring changes in occupancy, equipment, lighting, and window status in real time, a convolutional neural network (CNN)-based multi-head classification model was developed and trained with High Dynamic Range (HDR) images, collected using a low-cost fisheye camera in offices. A Python-based region of interest (ROI) generation program was developed to predefine the target areas for monitoring. The classification heads of the model were trained from scratch using a small office image dataset first; then, they were fine-tuned using another HDR image dataset collected in a larger open-plan office with more complex scenes, to evaluate the transferability of the model. The weighted mean classification precision and recall results showed that the developed model could classify the detailed status of each target heat gain (occupants, equipment, lighting, windows) in the predefined areas of the scene with great performance. Finally, to evaluate the impact of real-time monitoring of heat gains on energy demand, the open plan office space used for the experimental dataset collection was modeled using EnergyPlus software using (i) commonly assumed fixed schedules for occupancy, equipment and lighting and (ii) real-time monitored dynamic schedules for internal and solar gain components under the same weather conditions. The results showed that recommended fixed schedules may lead to significant errors in estimated internal and solar gains. The largest discrepancy was noted for occupancy and equipment usage, but other categories also showed both underestimation and overestimation of thermal load components. Implementing reliable and continuous monitoring of dynamic internal and solar heat gains is important for efficient demand-driven HVAC control.

Metal oxide particle electrodes for degradation of high concentration phenol wastewater via electrocatalytic advanced oxidation

High-concentration phenol wastewater is pollutant of concern that pose significant risks to human health and the environment. Three-dimensional electrocatalytic oxidation is one of the most promising wastewater treatment technologies because of its high treatment efficiency, low energy consumption and low secondary pollution. Lower-cost and higher-performance particles still faces great challenges. In this work, metal oxide particle electrodes were prepared using granular activated carbon (GAC) as a substrate to study the degradation of phenol by three-dimensional electrocatalytic oxidation. GAC particle electrodes loaded with different monometallic oxides (Mn, Fe, Co, Ce) and bimetallic oxides (Fe and Ce) were prepared by the impregnation method. The effectiveness of the particle electrodes in degrading phenol was greatly improved after active components loading. Among all monometallic oxide particle electrodes, the concentration degradation efficiency was in the order of Ce/GAC > Co/GAC >Mn/GAC > Fe/GAC, and the COD degradation efficiency was Ce/GAC > Fe/GAC > Co/GAC >Mn/GAC. After optimizing the loading metal type and loading amount, it was found that the 1.1% Fe-2.7% Ce/GAC particle electrode perform the best, with a phenol degradation efficiency of 95.48%, a COD degradation rate of 94.35%, an energy consumption of 0.75 kWh/kg COD. This lower-cost and higher-performance particle highlights a reliable route for solving the problem of particle electrode materials limiting the efficient treatment of phenol-containing wastewater.

Exploring body morphometry and weight prediction in Ganjam goats in India through principal component analysis.

The body conformations of 262 adult Ganjam goats were subjected to principal component analysis (PCA) with 11 morphometric variables. The results were then used to predict the mature body weight of the goats. Most of the traits were positively correlated, and the correlations were statistically significant. The three main components that the PCA recovered explained 76.12% of the variation in body morphometry overall. The first component accounted for approximately 54.74% of the overall variation and described almost all the traits except ear length and tail length, as indicated by high component loadings. The second component accounted for approximately 11.48% of the variation, mostly accounting for the variation in tail length. The principal component accounted for 9.89% and mostly explained the variation in ear length. The communalities ranged between 0.557 (horn length) and 0.848 (chest circumference) for the first three extracted components. The highest percentage of variability in chest girth was explained by the first three principal components, whereas it was the lowest for the horn length. In the context of predicting body weight through stepwise regression analysis, nine primary variables accounted for 57.3% of the total variance in body weight. Conversely, utilizing the first principal component alongside six additional principal components as independent variables resulted in capturing 56.3% of the variation in the adult live weight of goats while maintaining model comparability with other pertinent parameters. PCA was used efficiently for body weight prediction from major morphometric traits of Ganjam goats addressing the multicollinearity issue.

Effect of Residual Stresses on the Fatigue Stress Range of a Pre-Deformed Stainless Steel AISI 316L Exposed to Combined Loading

AISI 316L austenitic stainless steel is utilized in various processing industries, due to its abrasion resistance, corrosion resistance, and excellent properties over a wide temperature range. The physical and mechanical properties of a material change during the manufacturing process and plastic deformation, e.g., bending. During the combined tensile and bending loading of a structural component, the stress state changes due to the residual stresses and the loading range. To characterize the component’s stress state, the billet was bent to induce residual stress, but a phase transformation to martensite also occurred. The bent billet was subjected to combined tensile–bending and fatigue loading. The experimentally measured the load vs. displacement of the bent billet was compared with the numerical simulations. The results showed that during fatigue loading of the bent billet, both the initial stress state at the critical point and the stress state during the dynamic loading itself must be considered. Analysis was demonstrated only for one single critical point on the surface of the bent billet. The residual stresses due to the phase transformation of austenite to martensite affected the range and ratio of stress. The model for the stress–strain behaviour of the material was established by comparing the experimentally and numerically obtained load vs. displacement curves. Based on the description of the stress–strain behaviour of the pre-deformed material, guidelines have been provided for reducing residual tensile stresses in pre-deformed structural components.

Efficient optimization-based method for simultaneous calibration of load and resistance factors considering multiple target reliability indices

This study introduces an innovative optimization process for calibrating probabilistic load and resistance factors (LRFs) in limit state designs, effectively accommodating multiple target reliability indices. Given the impracticality of direct Monte Carlo simulations (MCS) for this task, a response surface method (RSM) is proposed to approximate load and resistance components separately rather than fitting conventional safety factors. This approach eliminates the need for additional implicit evaluations, thereby improving the efficiency of LRF calibration across multiple targets. The process is further enhanced by an adaptive boundary algorithm that updates search domains in real-time, streamlining the optimization. Validation through three examples—including one explicit and two implicit performance functions (a structural and a geotechnical example)—demonstrates that the method achieves accurate results with fewer iterations by dynamically narrowing search domains. Specifically, the accuracy of the proposed method is confirmed by comparing results with those from the literature for the explicit example and with basic MCS results applied to the initial implicit problems. Performance on the illustrative examples shows that the structural example achieves calibration for three targets within ten iterations. Additionally, this method eliminates the need for approximately ten thousand implicit evaluations when calculating limit state points for the geotechnical example.

Surface roughness measurement in the notch area for estimating dynamic component load bearing capacity

The surface of a part can have an essential influence on its dynamic load bearing capacity as grooves and scratches act as more or less significant imperfections or additional micro-notches, depending on the interpretation. In order to accurately predict the fatigue strength, detailed knowledge of the surface structure in critical areas is essential but cannot always be established due to geometric constraints. To overcome these restrictions, a method utilizing impressions and a laser scanning microscope to take surface topography measurements in critical notch areas is used in this work. It enables precise and reproducible measurements to be taken. The method is used to conduct surface roughness measurements on fatigue strength specimens made of four different materials. Several roughness influence factors are calculated based on the results and their influence on the result of a recalculation using a fatigue strength assessment is shown as example. The calculated fatigue limit is compared against experimental results. The comparison shows significant scatter in prediction accuracy and no optimal surface influence factor can be recommended so far. It has to be noted that only a small sample of specimens, which were not designed for researching surface roughness influence in particular, were available for this work. The effects of roughness in the notch may not be completely captured by the available roughness influence factors. Furthermore, issues arise in the correct implementation of stress concentration factor based roughness influence factors. Further research is required to provide a more comprehensive understanding of the effects of surface roughness on the fatigue limit.