Wire Arc Additive Manufacturing enables efficient fabrication of large-scale steel components with high geometric freedom. However, reliable structural use of WAAM high-strength steels requires an in-depth understanding of their cyclic behaviour, particularly in the low-cycle fatigue regime, which is dominated by plastic deformation. This study investigates the LCF behaviour of WAAM-fabricated ER100S-G high-strength steel using specimens extracted from thin-walled (WAAM-W) and solid deposits (WAAM-S). Strain-controlled fatigue tests were conducted at room temperature under strain ranges Δε, of 0.35% to 2.00%. WAAM-W was tested at strain ratios R_ε= −1, 0 and 0.5 while specimens from the WAAM-S configuration were evaluated at R_ε = 0. Although WAAM-S exhibited higher monotonic and cyclic strength, it underwent more pronounced cyclic softening, with a reduction of approximately 35% in the 0.2% offset cyclic yield stress, compared with 17% for WAAM-W. WAAM-S showed a steeper elastic strain-life slope, indicating that its strength advantage diminished with increasing fatigue life, while WAAM-W exhibited greater fatigue ductility and plastic-strain accommodation. This strength stability trade-off is attributed to the heterogeneous and banded bainitic morphology of WAAM-S, which provides cyclic strain localisation. In contrast, the more homogeneous WAAM-W microstructure and strain-induced transformation of retained austenite contribute to distributed plasticity and improved cyclic stability. For WAAM-W, positive mean strain reduced fatigue life at lower strain amplitudes due to a persistent tensile mean stress, whereas increased plasticity at higher amplitudes contributed to stress relaxation. The SWT formulation captured the mean-strain dependence of fatigue life, while the energy-based analysis linked fatigue life to the elastic and plastic strain-energy contributions.

In wind turbine towers, bolted flange connections are subjected to repeated cyclic loading, making fatigue a critical concern for structural integrity. This study experimentally and numerically investigates the fatigue behaviour of M48 bolted L-flange connections subjected to high preload introduced by tension-tightening. The preload initially reached approximately 95% of the bolt yield strength and stabilised at approximately 79% after tensioner removal. A total of 12 full-scale fatigue tests were conducted to quantify the influence of combined axial and bending loading, as well as preload relaxation during cyclic loading. The results show that high preload significantly improves fatigue life, particularly in the endurance-limit region, where existing design standards provide overly conservative predictions. Cyclic loading was found to accelerate preload relaxation, resulting in an increase in the tensile stress range in the bolt. To account for both bending effects and preload loss, an equivalent effective stress range was proposed, which reduced scatter in the fatigue data and provided a more consistent fatigue-damage parameter. In addition, a three-dimensional fracture-mechanics-based fatigue crack growth framework was developed to predict fatigue life. Bolt preload was introduced in the finite element model using a thermo-mechanical approach, and the model was validated against experimental results. Fatigue crack growth parameters were obtained from dedicated tests on specimens extracted from the bolt material. The numerically predicted S-N curves showed good agreement with experimental results and standard-based predictions in the finite-life regime. The proposed methodology improves fatigue-life prediction and supports more reliable design guidelines for preloaded wind turbine flange connections.

The authors regret that due to errors in typing, Eq. (2) and the corresponding texts should be replaced by: where ∆M is the bending moment range and the second moment of area [Formula presented], both in unit width. The results in the paper are not affected by the typing error. The authors would like to apologise for any inconvenience caused.

Bolted flange connections in wind turbine towers are subjected to cyclic loading, making fatigue a critical concern for their structural integrity. Bolt preload helps mitigate fatigue damage, but actual preload levels often deviate from design values due to uncertainties in the tightening process and geometric imperfections. This study evaluates the fatigue life of bolts L-flange connections under varying preload levels using a numerical fracture mechanics approach. A comprehensive three-dimensional finite element analysis (FEA) is conducted to assess the effects of preload on the stress intensity factor (SIF), crack propagation behaviour, and load transfer function (LTF). Additionally, the influence of thread helix angle, as well as combined axial and bending loads, on SIF and crack front evolution is examined. Experimental validation of the numerically obtained LTF is performed. A methodology for predicting S-N curves is proposed by deriving normalised solutions for LTF and SIF. The results indicate that increasing preload up to 90 % significantly reduces the SIF range, thereby decelerating crack growth and enhancing fatigue life. However, beyond 90 %, the improvement in fatigue life becomes less pronounced. Furthermore, the findings suggest that Eurocode 3 provides conservative fatigue life predictions, as it neglects bending effects, which are less detrimental than axial loading. Notably, even minor preload loss considerably shortens fatigue life, an effect that becomes more pronounced at higher preload levels. This research contributes to the development of predictive fatigue models for the bolted L-flange connection, providing insights into incorporating preload effects into fatigue life assessments.

Welding induces microstructural changes in the base metal, forming a heat-affected zone (HAZ) that is especially prone to strength degradation in high-strength steel (HSS) connections. While the mechanical behavior of welded joints is strongly influenced by the softened HAZ, most existing research has focused on its tensile and fatigue properties, with limited attention given to its shear behavior, despite evidence of shear failure mechanisms in certain welded structures. Building on recent advances in damage modeling, this paper calibrates a shear-modified Gurson-Tvergaard-Needleman (GTN) model tailored for the HAZ. The GTN model, which uses void volume fraction as a damage index and accounts for microvoids and stress triaxiality, is extended here to better capture shear-dominated failure modes. In the meantime, shear tests were conducted on coupon specimens extracted from butt-welded cold-formed rectangular hollow sections fabricated from three steel grades and three thicknesses. Load-deformation curves and local strain measurements are obtained from these shear tests. Finite element (FE) simulations of the HAZ, incorporating the shear-modified GTN model, are conducted. The experimentally measured load-deformation curves are used to calibrate the parameters of the shear-modified GTN model, while the measured local strains serve to validate the FE model. Practical values for the key parameters of the shear-modified GTN model are recommended for engineering applications. The estimated ultimate load carrying capacity based on the proposed model is in close agreement (approximately 5 %) with the experimental values. The limitations of the proposed model and directions for future research are also pointed out.

Monitoring fatigue damage in mechanical connections is essential for maintaining the safety and structural integrity of offshore wind turbines (OWTs), particularly during the early stage of crack initiation. Recently, the C1 wedge connection (C1-WC) has emerged as a promising innovation for use in OWTs. Acoustic emission (AE) monitoring is a widely used real-time technique for detecting fatigue cracks. The space limitations of the lower segment holes in the C1-WC presents challenges for detecting surface cracks with conventional AE sensors. Thin Piezoelectric Wafer Active Sensors (PWAS), while small and lightweight, face limitations due to their poor signal-to-noise ratio. In this study, we propose a baseline-based approach to enhance the effectiveness of PWAS for accurate AE monitoring in confined spaces. A benchmark model correlating the damage state of specimens is created by breaking pencil leads. Multivariate feature vectors are extracted and then mapped to the Mahalanobis distance for damage identification. The proposed method is validated through testing on compact specimens and C1-WC specimens. To enhance the AE detection results, supplementary monitoring techniques, including digital image correlation, crack propagation gauges, and distributed optical fiber sensors, are employed. The experimental setup, signal acquisition, and detection efficiency of these techniques are briefly outlined. This study demonstrates that the proposed approach is highly effective in detecting early damage in C1-WC specimens using AE monitoring with PWAS.

This research aims to experimentally investigate the ductile fracture characteristics and the level of anisotropy of four plates, 400 mm × 150 mm × 3.72 mm, made by Wire Arc Additive Manufacturing (WAAM) technology with 1 mm thick layers. Relatively small roughness is measured, expressed in maximum peak-to-valley height, measured by scanning, of 98 μm. Calibrated parameters for an advanced computational material model are derived for a finite element mesh size of 0.5 mm. The experimental campaign is based on eight types of short coupon specimens, analysed to explore fracture behaviour exposed to various stress conditions. Sixty-five coupon specimens, 51 milled and 14 tested in as printed conditions, cut out in three directions relative to the printing direction, are examined. The assumption of isotropic mechanical characteristics is confirmed. The mesoscale critical equivalent plastic strain (MCEPS) methodology is used to predict experimental results numerically. Three stages are considered: elastic, plastic, and couple plastic-damaged stages. The accuracy of the calibrated parameters is validated by comparing the engineering stress-strain relationships obtained from experimental tests and finite element (FE) analysis, reaching very good agreement. A list of all material parameters for ductile fracture modelling at various triaxiality levels and Lode parameters is provided for a mesh size of 0.5 mm.

The conceptual design of modular floating energy islands involves the integration of renewable energy sources, such as wind turbines and solar panels, onto floating structures. These modular systems offer advantages, such as minimal environmental impact and adaptability to different coastal and environmental conditions. This study focuses on the design of connections between the floating modules. Hinged joints are identified as a promising connector type due to their balance of flexibility and strength. Advanced numerical simulations are employed to assess the hydrodynamic forces and structural responses of floating platforms. A hinged connection model is numerically developed and the simulated performance shows good agreement with published results. Subsequently, three different connection configurations are evaluated, and their responses are discussed. Among these, a three-hinge connection system is found to be efficient, minimising relative displacement between the platforms.

Acoustic emission (AE) is widely used for identifying source mechanisms and the deformation stage of steel material. The effectiveness of this non-destructive monitoring technique heavily depends on the quality of the measured AE signals. However, the AE signals from deformation are easily contaminated by the signals from noise in a noisy environment. This paper presents a hybrid model for deformation stage identification, which combines a self-adaptive denoising technique and an Artificial neural network (ANN). In pursuit of model generality, AE signals were collected from tensile coupon tests with various steel materials and loading speeds. First, a decomposition-based denoising method is applied based on the singular spectral analysis (SSA) and variational mode decomposition (VMD), which is defined as SSA-VMD. Its effectiveness is demonstrated by simulated signals and experimental results. Following the use of the denoising technique, an ANN is constructed to identify the deformation stage of steel materials with the input of features extracted from the filtered AE signals. The results indicate that the ANN achieves a high prediction accuracy of 0.93 in the test set and 0.87 in unseen data. By applying this denoising method, the ANN-based approach enables accurate correlation of the collected AE signals to deformation stages. The finding can be used as the basis for the creation of new methodologies for monitoring structural health status of in-service steel structures.

The acoustic emission (AE) technique is commonly utilized for identifying source mechanisms and material damage. In applications requiring numerous sensors and limited detection areas, achieving significant cost savings, weight reduction, and miniaturization of AE sensors is crucial. This prevents excessive weight burdens on structures while minimizing interference with structural integrity. Thin Piezoelectric Wafer Active Sensors (PWAS), compared to conventional commercially available sensors, offer a miniature, lightweight, and affordable alternative. The low signal-to-noise ratio (SNR) of PWAS sensors and their limited effectiveness in monitoring thick structures result in the decreased reliability of a single classical PWAS sensor for damage detection. This research aims to enhance the functionality of PWAS in AE applications by employing multiple thin PWAS and performing a data-level fusion of their outputs. To achieve this, as a first step, the selection of the optimal PWAS is performed and a configuration is designed for multiple sensors. Pencil break lead (PBL) tests were performed to investigate the compatibility between selected PWAS and traditional WSα and R15α sensors. The responses of all sensors from different AE sources were compared in both the time and frequency domains. After that, convolutional neural networks (CNNs) combined with principal component analysis (PCA) are proposed for signal processing and data fusion. The signals generated by the PBL tests were used for network training and evaluation. This approach, developed by the authors, fuses the signals from multiple PWAS and reconstructs the signals obtained from conventional bulky AE sensors for damage detection. Three CNNs with different architectures were built and tested to optimize the network. It is found that the proposed methodology can effectively reconstruct and identify the PBL signals with high precision. The results demonstrate the feasibility of using a deep-learning-based method for AE monitoring using PWAS for real engineering structures.

Bolted connections are one of the key connection configurations in steel structures. The ductile fracture prediction is one of the challenges in the structural integrity evaluating of steel structures. To guarantee the safety of steel bridge in connections, an accurate assessment of the ultimate resistance of high-strength bolts under combined tensile-shear loads is necessary. However, the impacts of various parameters on high-strength bolts under combined tensile-shear loads are not sufficiently analysed in the existing references. Hence, in this paper, the validated mesoscale critical equivalent plastic strain (MCEPS) method is used to evaluate the ultimate resistance of high-strength bolts with different bolt grades, bolt diameters, bolt types, hole clearance, and preload force when the bolts are exposed to combined tensile-shear loading. The simulation results are compared to existing design specifications. Finally, the formula modifications in the existing design standards are proposed based on statistical analysis on numerical parametric results.

Fatigue cracks in the stiffener-to-deck plate connections of orthotropic bridge decks, initiating from the weld toe or root and propagating into the stiffener or weld throat, are experimentally and numerically studied. A statistical analysis of the structural stress is carried out using the experimental data. Automatic welded specimens show a significantly higher fatigue resistance than manual welded ones for both details of the study. Including results in the literature, the characteristic fatigue resistances appear larger than the values in current standards and range between 100 and 160 MPa. A proposal for the fatigue resistance values is given for design purposes. The effective notch stress, averaged strain energy density factor, and fracture mechanics methods are employed to study the sensitivity of the weld toe and root cracks for different (geometrical) variations, such as the lack of weld penetration. Among them, the fracture mechanics method agrees best with the experimental data. With the increase of weld penetration ratios from 75% to 100%, the fracture mechanics predicted fatigue resistances remain approximately equal for the weld toe cracks and increase for the weld root cracks.

The welding process induces residual stresses because of the non-uniform heating and cooling of the material. Residual stresses are known to influence fatigue crack growth. However, studies addressing both the formation of residual stress – giving a realistic multi-directional stress and strain field – and redistribution of residual stresses due to crack growth in the case of a surface crack have not been found. The extended finite element method was employed in this study to evaluate the stress intensity factors of a planar, growing crack in a welded T-joint with and without welding induced residual stresses. The initial residual stress field was taken from a welding simulation using the finite element method. The redistribution of the residual stress field due to crack growth was studied in addition to the shape and growth rate of the planar crack. The study shows that, in agreement with experimental evidence, the external stress ratio has a significant influence in the absence of residual stress but it does not have a significant influence in the presence of residual stress. The current study gives insight into the cause of this observation.