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Item type:Publication, Access status: with files , Analytical and machine learning-based fatigue life prediction of welded joints under multiaxial loadingEvaluating the fatigue life of welded joints under multiaxial loading is a key challenge in structural engineering. This study explores machine learning (ML) methods for predicting fatigue life and compares their performance against the novel super ellipse criterion, which is an analytical approach that aims to improve current design standard methods (e.g., Eurocode 3, IIW). Using a dataset of uniaxial and multiaxial fatigue tests with varying phase angles, ML models—including artificial neural networks and extreme gradient boosting (XGBoost)—are trained on features like stress amplitudes, phase differences, and material properties. Artificial neural networks provide high accuracy, while tree-based models like XGBoost offer better interpretability via model agnostic interpretation using Explainable Artificial Intelligence. Results show ML models can outperform traditional criteria, especially under non-proportional loading, but face limitations near the edges of the training data. This work highlights the potential and challenges of ML in fatigue prediction and highlights their value for enhancing the safety and reliability of welded structures.Publicationtype: Journal Article13 29 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Effective stress determination for flat bars with sharp notches by combining the theory of critical distances with artificial neural networks(Elsevier, 2026-04-17); ; ; ; This work systematically quantifies the deviations of gradient methods from the Theory of Critical Distances (TCD) and introduces neural-network-based metamodels for the rapid prediction of effective stresses in notched flat bars. Stress-based fatigue and fracture assessment methods often rely on the local stress field around a notch and are commonly referred to as effective stress or stress-gradient methods. In this study, notched flat bars are examined as a representative structural detail. The influence of geometrical variation, critical distance, loading, and plane state on the resulting effective stresses is investigated using the point, line, and area methods of TCD. Deviations of up to 40% are identified across certain geometry–loading combinations, highlighting the sensitivity of TCD based assessment to parameter variations. To eliminate the need for a dedicated numerical simulation for every new notch geometry, a series of feedforward artificial neural network (ANN) metamodels is developed and trained on thousands of finite element simulations. Particular attention is given to the effect of optimization algorithms on training performance and predictive robustness. The resulting metamodels provide fast and accurate estimates of effective stresses across a wide range of geometries and loading conditions, offering a computationally efficient alternative to repeated finite element analysis for the assessment of notched components.Publicationtype: Journal Article15 17 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Micro- and nanostructural investigations of high and ultra-high performance concrete under fatigue(Elsevier, 2025-04-30); ; A fine-grained UHPC, both undamaged and damaged by fatigue loading, was comparatively examined by various microstructural analytical methods, to evaluate the different techniques with respect to their applicability and relevance for the investigation of fatigue damage processes. The fatigue tests were stopped at the transition from phase II to phase III of the s-shaped strain development. The cyclic compression loading was performed with a frequency of ft = 1 Hz, and a stress level between Smin = 0.05 and Smax = 0.75 (fcm = 170.2 MPa). The fatigue process under these loading conditions is dominated by alterations and damages on the nano-scale, that can be observed by transmission electron microscopy. The resulting coarsening of the pore structure was also visible with dynamic vapor sorption. Nanoindentation indicates, that changes of the HD-C-S-H-phase occur. IR spectroscopy also indicates changes of the C-S-H phase and thermal analysis changes of the water content. Dynamic mechanical analysis (DMA) gave insight into the complex Young's modulus and Poisson's ratio changes. The acoustic emission technique gives information on the different processes during the single phases of fatigue and reveal a very different damage behaviour of dry and moist materials. Some microcracks are visible with light microscopy. It appears, that the number of cracks after fatigue is higher than before. With X-ray computed tomography, X-ray powder diffraction, the drying behaviour, the free water uptake, the water uptake under vacuum and by mercury intrusion porosimetry no significant differences between specimens with and without fatigue loading could be observed in this examination.Publicationtype: Journal Article16 14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Explainable machine learning-based fatigue assessment of 316L stainless steel fabricated by laser-powder bed fusionAdditive manufacturing (AM) and in particular laser-powder bed fusion has become a popular manufacturing techniques in recent years due to its significant advantages; however, the mechanical behavior of AM components often varies from components fabricated using conventional processes. For example, the fatigue behavior of components made by AM processes is heavily influenced by process-related defects and residual stresses in addition to applied stress amplitudes, stress ratio and surface conditions. Accounting for the interaction of these effects in fatigue design is difficult by means of traditional fatigue assessment concepts. Machine learning algorithms offer a possibility to account for such interactions and are easily applied once trained and validated. In this study, machine learning algorithms based on gradient boosted trees with the SHapley Additive exPlanation framework are used to predict defect location and fatigue life of additive manufactured AISI 316L specimens in as-built and post-treated manufacturing states, while also facilitating the understanding of the importance and interactions of various influencing factors.Publicationtype: Journal Article18 70 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Assessment of corrosion fatigue in welded joints using 3D surface scans, digital image correlation, hardness measurements, and residual stress analysis(Elsevier, 2023-11); ; ; Corrosion can significantly reduce fatigue resistance of steel constructions including offshore support structures. This can be attributed to either localized stress concentration caused by pitting corrosion or to embrittlement of the material during the corrosion process. In addition to the stress concentrations that can arise from pitting corrosion, offshore steel structures are characterized by a significant number of notches at weld seams, which may also cause stress concentrations. Therefore, it is of great importance to study the interaction between the pre-existing notches from welds and the notches from corrosion. Thus, reference material specimens as well as butt- and fillet-welded specimens from a mild steel S355 were investigated in this study. Before being stored in a salt spray chamber, the specimens were clean blasted as usually carried out for offshore support structures. After one month of exposure, the specimens were tested against fatigue and monitored by digital image correlation (DIC). The specimens were scanned with high-resolution 3D-scanners before and after corrosion exposure. In addition, material hardness and residual stresses were investigated to quantify the influence of corrosion on the material side and the influence from the welding process. It is shown that corrosion strongly influences the weld geometry. Both, individual pits and uniform corrosion are observed at the weld toe, which is relevant for fatigue. It was also shown that the fatigue strength of welded specimens depends not only on the geometry and its degradation by corrosion, but to a greater extent on the residual stresses present after corrosion. The residual compressive stresses applied by clean blasting were partly relieved by corrosion. The fatigue tests have shown increased fatigue strength after clean blasting and subsequent reduction due to corrosion. The fatigue strength of fillet welded specimen, for example, were increased from 74 N/mm2 in its as-welded condition to 158 N/mm2 through clean blasting. However, due to corrosion, fatigue strength decreases to 98 N/mm2.Publicationtype: Journal Article47 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Influence of pitting corrosion on the fatigue strength of offshore steel structures based on 3D surface scansSupport structures for offshore wind turbines and the corresponding transformer platforms are highly susceptible to corrosion. In particular, the phenomenon of pitting is crucial, as it leads to local stress concentrations and thus affects the fatigue life of structures. Despite corrosion protection systems, corrosion cannot be completely avoided, which can lead to pitting corrosion on the steel surface. This leads to fatigue life reduction, since the structures are exposed to high dynamic loads. Local stress concentrations can be considered in local concepts but so far, corrosion effects in local concepts are insufficiently defined. Hence, this paper aims to investigate the impact of pitting corrosion and the corresponding stress concentration on the fatigue life endurance of structural steel, used for offshore wind support structures. For this purpose, a total of 36 pre-corroded specimens with pitting were tested against fatigue failure and monitored with Digital Image Correlation. In addition, the specimens were scanned with a high-resolution 3D scanner and converted to numerical models by reverse engineering, to determine the stress concentrations on the surface. In most cases the hotspots from the numerical model coincide with the crack location detected with Digital Image Correlation. The notch effect has a significant impact on the crack location and crack path.Publicationtype: Journal ArticleScopus© Citations 26 96 308 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , An algorithm for statistical evaluation of weld toe geometries using laser triangulationCommonly, to evaluate the influence of the local weld geometry in fatigue test, small-scale specimens are used, assuming those represent a longer weld adequately. In this study, a comparison between short specimens and a long weld is performed. A method is developed for the statistical evaluation of weld toe radii and angles, stress concentration factors and weld quality classes. The results show a strong sampling rate dependence and lower ISO 5817:2014 weld quality results for higher sampling rates. Comparable results between short specimens and a long weld can be achieved using modal values of the parameters assuming a lognormal distribution.Publicationtype: Journal Article70 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Influence of rider weight, riding style and payload on the operating loads of electrically power assisted cycles (EPAC)(2021-08)Safety requirements for bicycles are defined in different standards in Europe and worldwide. Nevertheless the payload of bicycles and the weight of the rider are not taken into account for the formulation of the test loads for mechanical tests. The average weight of bicycle users varies widely worldwide, and in recent years there has been a significant increase, especially in the industrialized nations, where electrically power assisted cycles in particular are experiencing very high demand. The influence of the rider's weight and driving style is determined by operating load measurements, based on the current fatigue tests the component damage ratio under variable amplitude loading is calculated in order to create a basis for a future reformulation.Publicationtype: Journal Article71 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , A review of fatigue test data on weld toe grinding and weld profilingThe aim of this paper is to investigate the effects of weld toe grinding and weld profiling on the fatigue strength of welded joints and the main influencing factors. Thus, experimental test results of welds improved by different grinding techniques are reviewed. In total, 445 small- and full-scale fatigue test results of various weld types and steel grades with yield strengths up to 1100 MPa are analysed. The obtained improvements of two FAT classes correspond well with current guidelines; however, a new S–N curve slope of m = 4 is recommended—in line with proposals for other weld geometry improvement techniques.Publicationtype: Journal Article91 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Fatigue strength assessment of ground fillet-welded joints using 4R method(2021-01); ; ; In the present paper, a fatigue analysis for welded joints made of mild, and high- and ultra-high-strength steel grades and post-weld-treated with grinding and combined grinding and peening methods is carried out using literature data. The 4R method was used to re-analyze the data points considering material strength, residual stress, applied stress ratio, and weld toe radius. The use of the 4R method, instead of local fatigue strength assessment approaches, enabled residual stresses to be taken into consideration and accurately predicted the fatigue strength of joints in the as-welded and post-weld-treated conditions, including grinding and combined grinding and peening methods.Publicationtype: Journal Article108