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The mechanical tensile properties of T91 steel after being oxidized in liquid LBE saturated by oxygen at 350–500 °C for up to 1000 h are studied. Duplex oxide scales are found on T91 steels after the LBE exposure, and the thickness evolution is revealed. Oxidized T91 steels with surface oxide scale yield enhanced toughness and ductility, which is mainly attributed to the inner oxide layer. We show that the enhancing effect relies on the presence of the oxide scale, not its thickness. The possible enhancing effect of the oxide scale on the toughness of T91 steel by impeding the dislocation from emitting out of the steel surfaces is discussed and qualitatively verified by micro-scale elastoplastic simulations based on a newly proposed discrete dislocation dynamics and peridynamics superposition scheme.

In this paper, combined experimental and numerical methods are applied to investigate the effect of different contact stress and slip amplitude on the fretting fatigue (FF) behaviour of TC11 titanium alloy with gradient nanostructure introduced by ultrasonic surface rolling process (USRP). The properties of gradient nanostructure are characterized using scanning electron microscope (SEM), transmission electron microscope (TEM), X-ray residual stress tester, microhardness tester. As evidenced by the FF experimental results, USRP can significantly improve the FF life of TC11 by introducing large compressive residual stress and work hardening layer. The dominant parameters in improving the FF resistance under different loading conditions are investigated. In addition, the simulated results from Abaqus show that contact stress plays a more important role in influencing FF life compared to fretting bridge span, which is attributed to the change in slip amplitude and stress concentration. Fretting fatigue life initially increases and then decreases with contact stress and there is a contact stress threshold that leads to minimum lifetime. This threshold of USRP specimens is greater than that of untreated specimens. The transition of fretting mode along with contact stress is observed and discussed.

Abstract In this paper, the feasibility of Structural Health Monitoring (SHM) employing a novel Fibonacy Sequence (FS)-based Optimization Algorithms (OAs) and up-to-date computing techniques is investigated for a large-scale railway bridge. During recent decades, numerous metaheuristic intelligent OAs have been proposed and immediately gained a lot of momentum. However, the major concern is how to employ OAs to deal with real-world problems, especially the SHM of large-scale structures. In addition to the requirement of high accuracy, a high computational cost is putting up a major barrier to the real application of OAs. Therefore, this article aims at addressing these two aforementioned issues. First, we propose employing the optimal ability of the golden ratio formulated by the well-known FS to remedy the shortcomings and improve the accuracy of OAs, specifically, a recently proposed new algorithm, namely Salp Swarm Algorithm (SSA). On the other hand, to deal with the high computational cost problems of OAs, we propose employing an up-to-date computing technique, termed superscalar processor to conduct a series of iterations in parallel. Moreover, in this work, the vectorization technique is also applied to reduce the size of the data. The obtained results show that the proposed approach is highly potential to apply for SHM of real large-scale structures.

Abstract Alloy 718 is commonly used in the maritime and aerospace industries due to its strength and durability, particularly in engine rotating components such as disks, fan blades, and high-pressure compressors. As a new type of 3D printing technology, directed energy deposition (DED) can employ lasers to melt metal powders or wires to fabricate arbitrary-shaped workpieces directly from customized data, thereby making machining more synergistic and intuitive. However, the surface properties of the DED-printed alloy 718 samples, such as surface roughness and wear resistance, are typically subpar. By introducing severe plastic deformation to the near-surface, ultrasonic nanocrystal surface modification (UNSM) can be used as a post-processing method and results in altered properties. The uniaxial tensile test reveals that the UNSM-treated alloy 718 exhibits a higher mechanical property. Moreover, using a fretting test rig in accordance with the cylinder-on-plane agreement, a higher wear resistance for UNSM-treated alloy 718 is observed. This study employs the finite element method to fully comprehend the effect of UNSM on wear performance. The fretting wear process of Inconel 718 alloy is established using an energy-based finite element model. Considering the severe practical scenarios, the Johnson–Cook constitutive model is implemented, with the linear isotropic hardening model capturing the plastic behavior. In comparison to experimental measurements, the finite element results demonstrate unprecedented wear loss consistency with an error of less than 2%. Therefore, we conclude that the finite element model built in this study exhibits a high accuracy and can be used to analyze the effect of UNSM on fretting wear behavior. According to finite element analysis, as the normal load increases, the improvement in wear resistance induced by UNSM decreases. Given that the finite element model is based on the energy method, the effects of coefficient of friction (COF) and wear coefficient modified by UNSM are investigated separately. According to the findings, the UNSM-modified COF and wear coefficient play a significant role in determining the wear characteristics. Due to the removal of a substantial amount of material from the central area of the alloy 718 surface by wear, it is also possible to observe that severe plastic strains are primarily concentrated at the edges of the wear scars.

In recent years, bridge damage identification using a convolutional neural network (CNN) has become a hot research topic and received much attention in the field of civil engineering. Although CNN is capable of categorizing damaged and undamaged states from the measured data, the level of accuracy for damage diagnosis is still insufficient due to the tendency of CNN to ignore the temporal dependency between data points. To address this problem, this paper introduces a novel hybrid damage detection method based on the combination of CNN and Long Short-Term Memory (LSTM) to classify and quantify different levels of damage in the bridge structure. In this method, the CNN model will be used to extract the spatial damage features, which will be combined with the temporal features obtained from Long Short-Term Memory (LSTM) model to create the enhanced damage features. The combination successfully strengthened the damage detection capability of the neural network. Moreover, deep learning is also improved in this paper to process the acceleration-time data, which has a different amplitude at short intervals and the same amplitude at long intervals. The empirical result on the Vang bridge shows that our hybrid CNN-LSTM can detect structural damage with a high level of accuracy.

Abstract Optimization algorithms (OAs) are a vital tool to deal with complex problems, and the improvement of OA is inseparable from practical strategies and mechanisms. Among the OAs, Bee Algorithm (BA) is an intelligent algorithm with a simple mechanism and easy implementation, in which effectiveness has been proven when handling optimization problems. Nevertheless, BA still has some fundamental drawbacks, which can hinder its effectiveness and accuracy. Therefore, this paper proposes a novel approach to tackle the shortcomings of BA by combining it with Genetic Algorithm (GA). The main intention is to combine the strengths of both optimization techniques, which are the exploitative search ability of BA and the robustness with the crossover and mutation capacity of GA. An investigation of a real-life suspension footbridge is considered to validate the effectiveness of the proposed method. A baseline Finite Element model of the bridge is constructed based on vibration measurement data and model updating, which is used to generate different hypothetical damage scenarios. The proposed HBGA is tested against BA, GA, and PSO to showcase its effectiveness in detecting damage for each scenario. The results show that the proposed algorithm is effective in dealing with the damage assessment problems of SHM.

Abstract In this study, a meta-heuristic algorithm, named The Planet Optimization Algorithm (POA), inspired by Newton's gravitational law is proposed. POA simulates the motion of planets in the solar system. The Sun plays the key role in the algorithm as at the heart of search space. Two main phases, local and global search, are adopted for increasing accuracy and expanding searching space simultaneously. A Gauss distribution function is employed as a technique to enhance the accuracy of this algorithm. POA is evaluated using 23 well-known test functions, 38 IEEE CEC benchmark test functions (CEC 2017, CEC 2019) and three real engineering problems. The statistical results of the benchmark functions show that POA can provide very competitive and promising results. Not only does POA require a relatively short computational time for solving problems, but also it shows superior accuracy in terms of exploiting the optimum.

The dynamic experimental and numerical analysis of cracked beams has been studied with the aim of quantifying the influence of depth crack on the dynamic response of steel beams. Artificial Neural Method ANN has been used where a numerical simulation was improved in Matlab. A finite element model has also been developed by using the Ansys software, and the obtained results were compared with exact crack length. The study takes into account different hidden layer values in order to determine the sensitivity of the predicted crack depth. The results show that the response of the beam (frequencies) is strongly related to the crack depth, which significantly affects the beam behavior, especially when the crack is very deep where the ANN allows us to identify the crack in lower computational time. Based on the provided results, we can detect that the effect of hidden layer size can affect the results.

Modality and intermittent search strategy in combination with an Improve Particle Swarm Optimization algorithm (IPSO) to detect damage structure via using vibration analysis basic principle of a decline stiffness matrix a structure is presented in the study as a new technique. Unlike an optimization problem using a simplistic algorithm application, the combination leads the promising results. Interestingly, the PSO algorithm solves the optimal problem around the location determined previously, whereas Eagle Strategy (ES) is the charging of locating the position in intermittent space for the PSO algorithm to search local. ES can be easy to deal with its problem via drastic support of Levy flight. As know that the PSO algorithm has a fast search speed, yet the accuracy of the PSO algorithm is not as good as expected in many problems. Meanwhile, the combination is powerful to solve two trouble (1. Avoiding local optimization, 2. Receiving the result more accurate). The paper compares the results obtained from the PSO algorithm with the combination of IPSO and ES in some problems, as well as between experiment and FEM to demonstrate the effectiveness as mentioned. In which natural frequencies is used as the objective function to solve this optimization problem. The results present that the combination of IPSO and ES is quite effective.

The strongest point about damage identification based on the dynamic measurements, is the ability to perform structural health evaluation globally. Researchers in the last few years payed more attention to damage indicators based on modal analysis using either frequencies, mode shapes, or Frequency Response Functions (FRFs). This paper presents a new application of damage identification in a cross-ply (0�/90�/0�) laminated composite plate based on Force Residual Method (FRM) damage indicator. Considering single and multiple damages with different damage levels. As well as investigating the SSSS and CCCC boundary conditions effect on the estimation accuracy. Moreover, a white Gaussian noise is introduced to test the challenges of the technique. The results show that the suggested FRM indicator provides accurate results under different boundary conditions, favouring the SSSS boundary condition than the CCCC for 3% noise.

This paper presents issues related to the determination of the selected mechanical properties of adhesive joints made of polymeric pipes and the evaluation of the leak-tightness of the adhesive joints. The article attempts to demonstrate that the type of adhesive may affect the quality of adhesive joints in terms of both tightness and strength of joints. Five types of the polymer pipes differing in a polypropylene and a polyvinyl chloride, diameter and a wall thickness were used in the experiments. Two types of the adhesives were used to make the adhesive joints: Loctite 3430 A&B Hysol, a two-component epoxy adhesive, and Loctite 406, a one-component cyanoacrylate adhesive. Based on the leak-tightness tests results, it was possible to determine the quality of their adhesive joints without damaging the samples, while their tensile strength was determined through the strength tests. The tests performed allowed for the conclusion that the use of the polyvinyl chloride pipes and Loctite 406 one-component adhesive is recommended for this type of adhesive joints.

Abstract In this paper, a new method in forecasting the horizontal displacement of diaphragm wall (D.W.) for high-rise buildings is introduced. A new stochastic optimizer, called Planet Optimization Algorithm (P.O.A.), is employed to assess how proper finite element (F.E.) simulation is against field data. The process is adopted for a real phased excavation measured at the field. To automatically run the iterative optimization tasks, a source code is constructed directly in the Geotechnical Engineering Software (PLAXIS) by using Python to ensure that the operation between optimization algorithm and F.E. simulations are smooth to guarantee the accuracy of the complex calculation for the soil problem. The proposed process consists of two steps. (1) The parameters will be optimized at the early phases of the excavation. (2) The responses of D.W. displacements are forecasted at the subsequent phases. The aim of the process is to predict the displacements of D.W. of the building from the result of the nearby excavation or to provide early warning about the risks of excavation that may happen under vital phases. The proposed procedure also provides an effective method for optimization-based soil parameters updating in real engineering practice.

Metaheuristic algorithms have known vast development in recent years. And their applicability in engineering projects is constantly growing; however, their deferent exploration and exploitation techniques cause the engineering problems to favor some algorithms over others. This paper studies damage identification in steel plates using Frequency Response Function (FRF) damage indicator to detect and localize the healthy and damaged structure. The study is formulated as an inverse analysis, investigating the performance of three new metaheuristic algorithms of Wild Horse Optimizer (WHO), Harris Hawks Optimization (HHO), and Arithmetic Optimization Algorithm (AOA).� The objective function is based on measured and calculated FRF damage indicators. The results showed that the case of four damages with different damage severity levels presented a good challenge where the HWO algorithm was shown to have the best performance.� Both in convergence speed and CPU time.

In this paper, the initial and maximum load was studied using the Finite Element Modeling (FEM) analysis during impact testing (CVN) of pipeline X70 steel. The Gurson-Tvergaard-Needleman (GTN) constitutive model has been used to simulate the growth of voids during deformation of pipeline steel at different temperatures. FEM simulations results used to study the sensitivity of the initial and maximum load with GTN parameters values proposed and the variation of temperatures. Finally, the applied artificial neural network (ANN) is used to predict the initial and maximum load for a given set of damage parameters X70 steel at different temperatures, based on the results obtained, the neural network is able to provide a satisfactory approximation of the load initiation and load maximum in impact testing of X70 Steel.

Vibration-based damage detection tools are frequently employed because of their advantages over other non-destructive techniques. This paper presents an improved Frequency Response Function (FRF) indicator for damage identification in complex structures. To verify the effectiveness of the improved damage indicator, different structures are used, namely a 20-Bar Planar Truss (2D), a 28-Bar Space Truss (3D), a 72-Bar Space Truss, and a 600-bar Space Truss. The numerical models of all these structures are built in MATLAB using Finite Element Method (FEM). The improved indicator detects and localises single and multiple damages in the first stage. Next, after eliminating the healthy elements, it is used in a new objective function to solve the damage quantification problem using recent optimization techniques, including Artificial Gorilla Troops Optimizer (GTO), Dingo Optimization Algorithm (DOA), African Vulture Optimization algorithm (AVOA), and Gradient-Based Optimizer (GBO). The results demonstrate that all optimization techniques can accurately predict the exact level of damage. However, GTO is the most efficient in terms of convergence. To study the effectiveness of this indicator in case of noisy data, different levels of noise are considered in the damage assessment exercises.

This paper presents an enhanced artificial neural network (ANN) to predict the displacement in composite pipes impacted by a drop weight having different velocities. The impact response of fiber-reinforced polymer composite pipes depends on several factors including thickness, stacking sequence, and the number of layers. These factors were investigated in an earlier study using sensitivity analysis, and it was found that they had the most prominent effect on the impact resistance of the composite pipes. In this present study, composite pipes with a diameter of 54 mm are considered to explore the damages induced by low-velocity impact and the influence of these damages on their strength. To evaluate the effect of low-velocity, the pipes were exposed to impacts at different velocities of 1.5, 2, 2.5, and 3 m/s, and preliminary damage was initiated. Next, we used Jaya and E-Jaya algorithms to enhance the ANN algorithm for good training and prediction. The Jaya algorithm has a basic structure and needs only two requirements, namely, population size and terminal condition. Recently, Jaya algorithm has been widely utilized to solve various problems. Due to its single learning technique and limited population information, Jaya algorithm may quickly be trapped in local optima while addressing complicated optimization problems. For better prediction, an enhanced Jaya (E-Jaya) algorithm has been presented to enhance global searchability. In this study, ANN is enhanced based on the influential parameters using E-Jaya to test its effectiveness. The results showed the effectiveness of the E-Jaya algorithm for best training and prediction compared with the original algorithm.

The strongest point about damage identification based on the dynamic measurements, is the ability perform structural health evaluation globally. Researchers in the last few years payed more attention to damage indicators based on modal analysis using either frequencies, mode shapes, or Frequency Response Functions (FRFs). This paper presents a new application of damage identification in a cross-ply (0°/90°/0°) laminated composite plate based on Force Residual Method (FRM) damage indicator. Considering single and multiple damages with different damage levels. As well as investigating the SSSS and CCCC boundary conditions effect on the estimation accuracy. Moreover, a white Gaussian noise is introduced to test the challenge the technique. The results show that the suggested FRM indicator provides accurate results under different boundary conditions. Favouring the SSSS boundary condition than the CCCC for 3% noise.

Modality and intermittent search strategy in combination with an Improve Particle Swarm Optimization algorithm (IPSO) to detect damage structure via using vibration analysis basic principle of a decline stiffness matrix a structure is presented in the study as a new technique. Unlike an optimization problem using a simplistic algorithm application, the combination leads to promising results. Interestingly, the PSO algorithm solves the optimal problem around the location determined previously. In contrast, Eagle Strategy (ES) is the charging of locating the position in intermittent space for the PSO algorithm to search locally. ES is easy to deal with its problem via drastic support of Levy flight. As known, the PSO algorithm has a fast search speed, yet the accuracy of the PSO algorithm is not as good as expected in many problems. Meanwhile, the combination is powerful to solve two problems: 1) avoiding local optimization, and 2) obtaining more accurate results. The paper compares the results obtained from the PSO algorithm with the combination of IPSO and ES for some problems and between experiment and FEM to demonstrate its effectiveness. Natural frequencies are used in the objective function to solve this optimization problem. The results show that the combination of IPSO and ES is quite effective.

The dynamic experimental and numerical analysis of cracked beams has been studied with the aim of quantifying the influence of depth crack on the dynamic response of steel beams. Artificial Neural Method ANN has been used where a numerical simulation was improved in Matlab. A finite element model has also been developed by using the Ansys software, and the obtained results were compared with exact crack length. The study takes into account different hidden layer values in order to determine the sensitivity of the predicted crack depth. The results show that the response of the beam (frequencies) is strongly related to the crack depth which significantly affects the beam behavior, especially when the crack is very deep where the ANN allows us to identify the crack in lower computational time. Based on the provided results, we can detect that the effect of hidden layer size can affect the results.

A method combining crystal plasticity (CP), the eXtended Finite Element Method (XFEM), and cohesive zone model (CZM) with traction separation law is developed for an Al-Cu-Mg alloy to predict the effect of grain orientation on fatigue crack propagation (FCP) during stage ΙΙ within a single crystal. The simulation results show that of all the orientations, Goss grain possesses the largest fatigue crack deflection, then followed by Cube orientation. Comparatively, Brass, Copper, S and Random grains have relatively small crack deflection angles. Besides, it is found that Goss grain with the largest fatigue crack deflection possesses the lowest FCP rate as compared with other orientations. The results of simulations are consistent with previous experimental observations. This indicates that this coupled CP XFEM CZM simulation method is capable of well predicting the effect of grain orientation on FCP during fatigue stage ΙΙ of Al-Cu-Mg single crystal.

The main cable is the primary load-bearing component of a long-span multi-tower suspension bridge. The interaction between a dead load, vehicle load, wind load, and the corrosion environment leads the main cable wire to exhibit tribo-corrosion-fatigue behaviors. This behavior causes wire wear and deterioration, as well as a reduction in the effective cross-sectional area. This leads to the gradual deterioration of the wire’s load-bearing strength and seriously affects the load-bearing safety of the main cable. In order to ensure the safety of suspension bridges, it is critical to investigate the gradual deterioration behavior of the main cable wire’s load-bearing strength. A wire tribo-corrosion-fatigue test rig was established to test the wire under different friction pairs (saddle groove or parallel wires). The cross-sectional failure area of the wire with different pairs was obtained by super-depth electron microscopy and calculation. The damage degree evolution model and the deterioration model of the wire load-bearing strength were established by combining the theory of damage mechanics and the finite element method. The results show that, as contact and fatigue loads increase, so does the cross-sectional failure area of the fatigue steel wire. The fatigue wire’s damage degree has a good quadratic function relationship with fatigue cycles. The damage degree of the wire increases and the load-bearing strength decreases with increasing contact load and fatigue load. The load-bearing strength of the wire changes little at the beginning and decreases with increasing fatigue cycles. The results have fundamental significance for the life prediction of the main cable wires of suspension bridges.

Stress formation of monocrystals and bicrystals is investigated in specific oriented grains and grain boundaries of AA2024 alloy by using Crystal Plasticity Finite Element Method (CPFEM). The simulations show that the maximum Schmid factor (SF) value and the number of equivalent initial slip system (EISS) play a principal role in controlling the magnitude of internal stress within monocrystals. For bicrystal model, Goss and Cube grains are not the best ones for relieving stress concentration caused by their orientations, but they are the best ones for relieving grain boundary (GB) stress concentration. To this end, the dependence relations are discussed between GB stress and an advanced comprehensive factor combining SF, and geometry compatibility factor for 5 independent slip systems. It is found that this proposed comprehensive factor considering the contribution from 5 to independent slip systems effectively improves its dependence on GB stress.

Discrete wavelet transform is a useful means for crack identification of beam structures. However, its accuracy is severely dependent on the selecting mother wavelet and vanishing moments, which raises a significant challenge in practical structural crack identification. In this paper, a novel approach is introduced for structural health monitoring of beams to fix this challenge. The approach is based on the combination of statistical characteristics of vibrational mode shapes of the beam structures and their discrete wavelet transforms. First, this paper suggests using regression statistics between intact and damaged modes to monitor the health of beam structures. Then, it suggests extracting quasi-Pearson-based mode shape index of the beam structures to use them as an original signal in discrete wavelet transforms. Findings show that the proposed approach has several advantages compared with the conventional mode shape signal processing by the discrete wavelet transforms and significantly improves damage detection’s accuracy.

Structural Health Monitoring is a process of continuous evaluation of infrastructure status. In order to be able to detect the damage status, data collected from sensors have to be processed to identify the difference between the damaged and the undamaged states. In recent years, convolution neural network has been applied to detect the structural damage and with positive results. This paper proposes a new method of damage detection using combination of deep neural networks. The method uses a convolution neural network to extract deep features in time series and Long Short Term Memory network to find a statistically significant correlation of each lagged features in time series data. These two types of features are combined to increase discrimination ability compared to deep features only. Finally, the fully connected layer will be used to classify the time series into normal and damaged states. The accuracy of damaged detection was tested on a benchmark dataset from Los Alamos National Laboratory and the result shows that hybrid features provided a highly accurate damage identification.

The hoisting rope in the kilometer-deep coal mine exhibits the tension–torsion fretting fatigue behaviors of inclined crossed steel wires in acid solution. Distinct contact load and torsion angles of steel wires in the rope cause different crack propagation behaviors, which greatly affect the fatigue lives of steel wires. Therefore, the effects of contact load and torsion angle on the crack propagation behaviors of inclined crossed steel wires during tension–torsion fretting fatigue in acid solution were investigated in the present study. The three-dimensional X-ray tomographic micro-imaging system was used to reveal evolutions of crack profiles and crack propagation depths during the test. The evolution of friction coefficient between steel wires during the test is presented. The three-dimensional white light interference microscope, electrochemical analyzer, and scanning electron microscope were employed to investigate the wear depth profiles, Tafel polarization curves and impedance spectra, and wear scar morphologies, respectively, of steel wires. Effects of contact load and torsion angle on crack propagation behaviors of inclined crossed steel wires during the tests were explored through analyses of friction and wear mechanisms and electrochemical corrosion damage. The results show that as the contact load and torsion angle increase, the crack propagation depth and rate of steel wire both increase and the fatigue life of steel wire decreases. Those are mainly attributed to the increases in the average tangential force between steel wires, wear depth, electrochemical corrosion tendency, and surface damage of steel wire as well as the decrease in corrosion resistance.

The effects of vibration on the emergency braking tribological behaviors of the brake shoe of a deep coal mine hoist were investigated in this study. The thermal, frictional and mechanical parameters of the brake shoe were obtained. The vibration characteristics of the brake shoe during emergency braking were investigated, employing multibody dynamics analysis. The effect of vibration on the emergency braking tribological behaviors (temperature and stress distributions) of brake interfaces was explored using the finite element method. The self-made tribo-brake test rig of a brake shoe was employed to reveal the friction deterioration behaviors of the brake shoe during emergency braking. The results show obvious vibrations of all brake shoes along the direction of positive braking pressure during emergency braking. The vibration causes increases in the equivalent Von Mises stress and temperature at the contact interfaces between the brake disc and the brake shoe as compared to the case of ignoring the vibration. Along the rotation direction of the brake disc, the equivalent stress and temperature of the brake disc surface present three overall rapid increases, as well as two slight decreases during emergency braking. As compared to cyclic emergency braking, continuous emergency braking exhibits more obvious tribological degradation of the brake shoe, attributed to enhanced vibration. The wear loss of the brake shoe increases with increasing emergency braking cycles and continuous emergency braking time.

Identifying cracks in the incipient state is essential to prevent the failure of engineering structures. Detection methods relying on the analysis of the changes in modal parameters are widely used because of the advantages they present. In our previous research, we found that eigenfrequencies were capable of indicating the position and depth of damage when sufficient vibration modes were considered. The damage indicator we developed was based on the relative frequency shifts (RFS). To calculate the RFSs for various positions and depths of a crack, we established a mathematical relation that involved the squared modal curvatures in the healthy state and the deflection of the healthy and damaged beam under dead mass, respectively. In this study, we propose to calculate the RFS for beams with several cracks by applying the superposition principle. We demonstrate that this is possible if the cracks are far enough from each other. In fact, if the cracks are close to each other, the superposition method does not work and we distinguish two cases: (i) when the cracks affect the same beam face, the frequency drop is less than the sum of the individual frequency drops, and (ii) on the contrary, cracks on opposite sides cause a decrease in frequency, which is greater than the sum of the frequency drop due to individual damage. When the RFS curves are known, crack assessment becomes an optimization problem, the cost function being the distance between the measured RFSs and all possible RFSs for several vibration modes. Thus, the RFS constitutes a benchmark that characterizes damage using only the eigenfrequencies. We can accurately locate multiple cracks and estimate their severity through experiments and thus prove the reliability of the proposed method.

The long-span multi-tower suspension bridge is widely used in the construction of river and sea crossing bridges. The load-bearing safety and anti-sliding safety of its main cable are directly related to the structural safety of a suspension bridge. Failure mechanisms of the main cable of a long-span multi-tower suspension bridge are discussed. Meanwhile, the tribo-corrosion-fatigue of main cable, contact, and slip behaviors of the saddle and service safety assessment of the main cable are reviewed. Finally, research trends in service safety assessment of main cable are proposed. It is of great significance to improve the service safety of the main cable and thereby to ensure the structural safety of long-span multi-tower suspension bridges.

Los modelos analíticos disponibles para calcular las fuerzas patelofemorales de la rodilla están limitados al movimiento estándar de cuclillas cuando el centro de gravedad es fijo horizontalmente. En este artículo se presenta un modelo para calcular con exactitud las fuerzas patelofemorales, teniendo en cuenta el cambio de posición del centro de gravedad del tronco durante una sentadilla profunda (posición de sentadilla modificada). Se valida la exactitud del modelo desarrollado mediante comparaciones con resultados de la técnica de dinámica inversa.

Low cycle fatigue (LCF) and creep fatigue interaction (CFI) loadings are the main factors resulting in the failure of many critical components in the infrastructure of power plants and aeronautics. Accurate prediction of life spans under specified loading conditions is significant for the design and maintenance of components. In the present study, various LCF and CFI tests are conducted to investigate the effects of temperature, strain amplitude, hold time and hold direction on the fatigue life of P92 steel. To predict fatigue life under different experimental conditions, various conventional life prediction models are evaluated and discussed. Moreover, a new empirical life prediction model is proposed based on the conventional Manson-Coffin-Basquin (MCB) model. The newly proposed model is able to simultaneously consider the effects of temperature, strain amplitude, hold time and hold direction on predicted life. The main advantage is that only the known input experimental parameters are required to perform the prediction. In addition to the validation made through the experimental data of P92 steel conducted in the present paper, the model is also verified through numerous experimental data reported in the literature for various 9⁻12% Cr steels.

A direct approach for the calculation of the natural frequencies and vibration mode shapes of a perfectly clamped-free beam with additional stepwise eccentric distributed masses is developed, along with its corresponding equations. Firstly there is contrived influence of a mass, located on a given position along the beam, upon the modal energies, via an energy analysis method. Secondly, the mass participation coefficient is defined as a function of the mass location and the bending vibration mode number. The proposed coefficient is employed to deduce the mathematical relation for the frequencies of beams with supplementary eccentric loads, generally available for any boundary conditions. The accuracy of the obtained mathematical relation was examined in comparison with the numerical simulation and experimental results for a cantilever beam. For this aim, several finite element models have been developed, individualized by the disturbance extent and the mass increase or decrease. Also, one real system was tested. The comparisons between the analytically achieved results and those obtained from experiments proved the accuracy of the developed mathematical relation.

The analysis of fretting fatigue plays an important role in many engineering fields. The presence of heterogeneity may affect the performance of a machine or a structure, including its lifetime and stability. In this paper, the effect of randomly distributed micro inclusions on the fretting fatigue behaviour of heterogeneous materials is analysed using the finite element method (FEM) for different sizes, shape and properties of inclusions. The effect of micro inclusions on macroscopic material properties is also considered by representative volume element (RVE). It is shown that the influence of micro inclusions on macroscopic material properties cannot be ignored, and the shape and size of the inclusions have less effect on the macroscopic material properties as compared to the material properties of inclusion and volume ratio. In addition, various parameters of inclusions have little effect on the peak tensile stress, which remains almost the same as homogeneous material. Peak shear stress occurs at many places inside the specimen, which can result in multiple cracking points inside the specimen, as well as at the contact surface. Moreover, the stress band formed by the stress coupling between adjacent inclusions may have an important influence on the direction of crack growth.