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The paper presents a comparison of the results of the fatigue crack growth rate for raw rail steel, steel reinforced with composite material—CFRP—and also in the case of counteracting crack growth using the stop-hole technique, as well as with an application of an “anti-crack growth fluid”. All specimens were tested using constant load amplitude methods with a maximum loading of Fmax = 8 kN and stress ratio R = σmin/σmax = 0.1 in order to analyze the efficiency of different strategies of fatigue crack growth rate deceleration. It has been shown that the fatigue crack grows fastest in the case of the raw material and slowest in the case of “anti-crack growth fluid” application. Additionally, the study on fatigue fracture surfaces using light and scanning electron (SEM) microscopy to analyze the crack growth mechanism was carried out. As a result of fluid activity, the fatigue crack closure occurred and significantly decreased crack driving force and finally resulted in fatigue crack growth decrease.

Current research studies take a mechanism-based approach rather than a purely empirical one to identify factors pertinent to manufacturing processes and composition/microstructure parameters that influence the fatigue performance of advanced materials and, hopefully, to guide further improvement and the insertion of new materials for advanced engineering applications. In this special issue, Fatigue in Advanced Materials, twenty-seven research articles are published. The subjects are multidisciplinary and divided into several topics including: (i) fatigue and fracture of additively manufactured materials, (ii) microstructure, micro-scale effects, and mechanical properties, (iii) surrogate approaches applied to mechanical properties relevant to fatigue and fracture, (iv) probabilistic modeling for fatigue life prediction, (v) analytical and numerical fatigue approaches of materials and components, and (vi) other related themes. The Guest Editors would like to express gratitude to all authors for their contributions and to all anonymous reviewers for their generous work that is fundamental in the dissemination of scientific findings.

This study presents an advanced framework for modeling the lateral-torsional buckling behavior of cellular steel beams, which combines hybrid intelligent models with numerical simulation. The proposed hybrid intelligent models employ a large dataset-based finite element method (FEM) for training and validation the framework, as well as metaheuristic algorithms for optimal auto-hyper-parameters selection. A total of 1535 numerical models are examined in order to evaluate the lateral-torsional buckling behavior. Following that, the least square support vector machine (LSSVM) optimized using four metaheuristic algorithms (ME): particle swarm optimization (PSO), ant lion optimization (ALO), grey wolf optimizer (GWO), and Harris hawks optimization (HHO) algorithms, is utilized to estimate accurately the lateral-torsional buckling resistance. According to the findings of a comprehensive performance evaluation utilizing statistical and graphical comparing criteria, the suggested LSSVM-ME predicts the lateral-torsional buckling behavior with excellent accuracy. LSSVM-HHO, in particular, outperforms the other hybrid intelligence models, with an RMSE of 41.72 kN.m and an NSE of 0.99. Overall, the results indicate that the proposed framework has a great potential for use as a practical tool for estimating the lateral-torsional buckling behavior of cellular steel beams.

This research work approaches the prediction of the ultimate transverse strength behavior of unidirectional composite laminas (Yt) using mixed artificial neural network (ANN) models. Thereby, two empirical relations, presented by Teanu and Barbero, were used for comparison purposes and to construct the mixed ANNs. These equations differ in the mechanical properties used to obtain Yt. For the ANN training, a dataset with 85 laminas was used, all obtained from the literature and manufactured with different types of fibers (aramid, glass and carbon) and polymer matrices. The best model obtained was the one based on the relation presented by Barbero, where the void fraction volume (Vv) and the transverse modulus of the fiber (Eft) are input parameters. Another aspect was the discovery of a region in the dataset that drastically changes its behavior, forming a valley in the prediction curves with values far lower than those forecast by empirical equations. This approach can be seen as a new methodology to estimate Yt using the same variables encountered in the classical equations but obtaining values closer to experimental ones.

In this paper, a brief review of the main fatigue design criteria and some advanced fatigue approaches applied to offshore structures (e.g. offshore wind turbines) are presented. It is extremely important to understand the fatigue phenomenon and how it affects structures since offshore structures are constantly submitted to cyclic loading and corrosive attacks that aggravate the problem. All the influencing factors and approaches used during the design phase are also discussed.

A new formulation of a Logistic deterministic S-N curve is applied to fatigue data of metallic materials from ancient Portuguese riveted steel bridges. This formulation is based on a modified logistic relation that uses three parameters to fit the low-cycle- (LCF), finite-life- and high-cycle-fatigue (HCF) regions. This model is compared to the Kohout-Věchet fatigue model, which has a refined adjustment from very low-cycle fatigue (VLCF) to very high-cycle fatigue (VHCF). These models are also compared with other models, such as, Power law and fatigue-life curve from the ASTM E739 standard. The modelling performance of the S-N curves was made using the fatigue data considering the stress fatigue damage parameter for the materials from the Eiffel, Luiz I, Fão and Trezói riveted steel bridges. Using a qualitative methodology of graphical adjustment analysis and another quantitative using the mean square error, it was possible to evaluate the performance of the mean S-N curve formulation. The results showed that the formulation of the S-N curve using the Logistic equation applied to the metallic materials from the old bridges obtained superior performance to the analysed models, both in the estimation of fatigue behaviour in the low-cycle fatigue (LCF) region and in the lowest mean square error.

This paper presents a research about the monotonic and failure behaviours of bolted joints made of thin plates of S350GD and S355MC steels grades, which are used for rack structures. A full factorial test matrix was performed considering two joints configurations (1+1 and 4+4 double shear bolted joints), two material thicknesses (2 and 3mm), three coatings (“black steel”, zinc coating, zinc plus paint) and two preload levels (25%×70%Fu and 70%Fu). Tests were performed under static monotonic loading until failure. Slip tests were also performed according to the EN 1090-2 standard to evaluate joint slip factors for the three material surface conditions. In addition to the experimental tests, numerical simulation of static tests were performed using elastoplastic material behaviour, based on Mises yield theory and isotropic hardening identified with experimental tensile testing data. The models were able to reproduce conveniently the ultimate loads of the joints and failure modes, including clamping and friction effects.

This paper proposes a strain prediction method for wind turbine blades using genetic algorithm back propagation neural networks (GA-BPNNs) with applied loads, loading positions, and displacement as inputs, and the study can be used to provide more data for the wind turbine blades’ health assessment and life prediction. Among all parameters to be tested in full-scale static testing of wind turbine blades, strain is very important. The correlation between the blade strain and the applied loads, loading position, displacement, etc., is non-linear, and the number of input variables is too much, thus the calculation and prediction of the blade strain are very complex and difficult. Moreover, the number of measuring points on the blade is limited, so the full-scale blade static test cannot usually provide enough data and information for the improvement of the blade design. As a result of these concerns, this paper studies strain prediction methods for full-scale blade static testing by introducing GA-BPNN. The accuracy and usability of the GA-BPNN prediction model was verified by the comparison with BPNN model and the FEA results. The results show that BPNN can be effectively used to predict the strain of unmeasured points of wind turbine blades.