Probability Of Fatigue Failure Research Articles

Nondeterministic High-Cycle Fatigue Macromodel Updating and Failure Probability Analysis of Welded Joints of Long-Span Structures

Nondeterministic High-Cycle Fatigue Macromodel Updating and Failure Probability Analysis of Welded Joints of Long-Span Structures

Probabilistic fatigue life prediction of corroded PC beams considering spatial variability of pitting corrosion

Pitting corrosion intensifies local stress in materials, accelerating the fatigue crack initiation and propagation at the pit root. The randomly distributed corrosion pits can result in fatigue fractures in rebars and wires at non-critical sections, which further increases the fatigue failure probability of structures. However, previous studies have scarcely taken this into account. Here, we propose a novel model for fatigue life prediction of corroded post-tensioned prestressed concrete (PC) beams, considering the spatial variability of pitting corrosion. First, a Weibull distribution model of the pitting factor is developed based on the 3D laser scanning method. Then, the 3D non-Gaussian random field of the pitting factor in PC beams is generated using the inverse Nataf transformation. Next, the 3D pit-induced stress concentration is modeled using pit shape ratios. Following that, the initiation and propagation of fatigue cracks in pitting corroded wires and rebars are considered by the equivalent initial flaw size methodology. Finally, the fatigue analyses of the pitting corroded wires and rebars in all beam elements are conducted step by step until the load-bearing capacity of PC beams becomes lower than the applied load. The proposed prediction model is validated using the experimental data. The results reveal that neglecting the spatial variability of pitting corrosion overestimates the fatigue life of corroded PC beams, especially under high corrosion degrees.

Data-Driven Fatigue Failure Probability Updating of OSD by Bayesian Backward Propagation

This study introduces a data-driven approach for updating the fatigue failure probability of the orthotropic steel deck (OSD) using Bayesian backward propagation. The OSD in steel bridges is considered as a parallel system composed of two critical fatigue-prone components, namely, the rib-to-diaphragm and rib-to-deck joints. A probabilistic model for fatigue reliability is established based on the equivalent structural stress method and limit state function. The system-level fatigue reliability model is then constructed, taking into account the correlations between limit states of individual components through Bayesian network forward propagation. The key advantage of the Bayesian network-based framework is its ability to perform backward propagation, allowing for the updating of failure probabilities for critical components when the system-level failure of the OSD is observed. Consequently, the proposed approach enables the identification of vulnerable components through data-driven fatigue failure probability updating. Finally, the approach is applied to a real instrumented steel bridge to determine the time-dependent fatigue failure probability at both the system and component levels over its service life. The results show that the component-level fatigue failure probability model will underestimate the fatigue life in comparison to the system-level model. Meanwhile, the proposed method could identify vulnerable components by quantifying the fatigue failure probability of in-service steel bridges.

Fatigue Reliability Assessment of RC Beams in Heavy-Haul Railways Based on Point Estimate Method.

Heavy-haul railways have a high passing frequency of trains with a large axle weight, causing rapid accumulation of fatigue damage in reinforced concrete (RC) bridge structures, which significantly affects the safety of the bridges. To study the fatigue reliability of RC beams in heavy-haul railways, the fatigue performance function for RC beams in heavy-haul railways was established, and the fatigue reliability assessment method for bridge structures in heavy-haul railways based on the point estimate method (PEM) was developed. An 8 meter-span plate beam in an existing heavy-haul railway illustrates the method. The train axle weight and dynamic coefficient were considered random variables, and the first four moments of equivalent stress ranges were obtained. The traffic quantity of the heavy-haul railways was investigated, and the fatigue reliability was evaluated using the proposed method. In addition, the effects of annual freight volume and train axle weight on fatigue reliability were discussed. Results indicate that PEM can effectively and accurately evaluate the fatigue reliability of RC beams in heavy-haul railways. In the first 20 years of operation, the fatigue failure probability was less than the limit value specified in the standard. The increase in annual traffic volume and train axle weight will cause a significant increase in fatigue failure probability. The research results of this paper are expected to provide an important basis for the design and maintenance of reinforced concrete bridges for heavy-haul railways in the future.

Probabilistic machine learning approach to reliability analysis of a bogie frame under dynamic loading

ABSTRACT This study aims to evaluate the fatigue failure probability of a bogie frame considering the variability of input parameters, including loading (), endurance limit (), and fillet size (), through a data-driven surrogate model. Mechanical tests were conducted to determine the mechanical properties of the material of the bogie frame while a combination of machine learning and FEA has been utilized to generate a dataset for the dynamic response of the bogie frame under main in-service fatigue loads. Nine machine learning-based surrogate models were constructed based on the actual response at a limited set of data points chosen by the Optimum space-filling algorithm, and their accuracy was investigated. It is found that the CatBoost model is the optimal algorithm to map the stochastic input parameters with the factor of safety as the output parameter and perform the reliability evaluation. Also, results reveal a fatigue reliability of 99.34% for the bogie frame under normal conditions, with a cumulative failure probability of less than 0.66% over a 30-year service life. Furthermore, the results show that the proposed machine learning-based approach is an efficient tool to evaluate the fatigue failure probability of the bogie frame with reasonable accuracy when a small set of training data is available. This study’s scope extends to providing comprehensive guidelines for employing machine learning methods for fatigue reliability analysis of complex vehicle structures in the presence of various stochastic variables.

A Bayesian defect-based physics-guided neural network model for probabilistic fatigue endurance limit evaluation

Accurate fatigue assessment of material plagued by defects is of utmost importance to guarantee safety and service continuity in engineering components. This study shows how state-of-the-art semi-empirical models can be endowed with additional defect descriptors to probabilistically predict the occurrence of fatigue failures by exploiting advanced Bayesian Physics-guided Neural Network (B-PGNN) approaches. A B-PGNN is thereby developed to predict the fatigue failure probability of a sample containing defects, referred to a given fatigue endurance limit.In this framework, a robustly calibrated El Haddad’s curve is exploited as the prior physics reinforcement of the probabilistic model, i.e., prior knowledge. Following, a likelihood function is built and the B-PGNN is trained via Bayesian Inference, thus calculating the posterior of the parameters. The arbitrariness of the choice of the related architecture is circumvented through a Bayesian model selection strategy. A case-study is analysed to prove the robustness of the proposed approach. This methodology proposes an advanced practical approach to help support the probabilistic design against fatigue failure.

Determination of Size Factor Influencing on Evaluation of F atigue Life of Steel Member

Irrational elements inside of structure become greater with the increase of size so this results in higher probability of fatigue failure. Especially, cracks easily occur in the sections with comparatively high stress. Since ordinary tensile and compressive fatigue experiment can’t provide clear size effect for smooth specimen, it can cause great error in correction of the fatigue strength of a structural member. This paper analyzed size factors of geometrically similar notched specimen based on finite element method and Theory of critical distance (TCD). First, simple formula was presented to calculate size factor of notched specimen by gradient effect, and introduced it to an example to get size factor. Calculation results show it is reasonable to apply size factor to the notched specimen and it is better to reflect size effect under tension and compression. Under the assumption that the life of notched plane mainly depend on the formation of cracks life span formula for steel materials was prepared. And empirical formula for fatigue strength of notched specimen with local feature variable is presented. These formulas and empirical extrapolation were used as the base to determine the formula for size factor of notched specimen. Fatigue simulation and law of approximation were applied to produce a model of skip structure in mine and formula of size factor was used to calculate size factor of member.

Fuzzy dynamic Bayesian network based on a discrete aggregation method for risk assessment of marine nuclear power platform hinge joints accidents

Hinge joints play a critical role in the positioning system of Marine Nuclear Power Platforms (MNPP) by facilitating the transmission of mooring forces and accommodating rotational motions of the hull. Failure of hinge joints during operation can result in severe consequences for both the environment and human safety. Therefore, this study aims to address the limitations associated with the dynamic characteristics and uncertain information pertaining to the risk systems of hinge joint structures in MNPP. To tackle the challenge of conflicting expert opinions in group decision-making, a novel method called the discrete aggregation method (DAM) is proposed. DAM utilizes fuzzy opinion aggregation, taking into account the degree of consensus among experts. This approach effectively overcomes the problem of conflicting opinions. Furthermore, building a hinge joint accidents model using Bow-tie (BT) for hazard identification, and then mapping the BT model to a dynamic Bayesian networks (DBN) model. Fuzzy set theory (FST) and DBN are employed to handle cognitive uncertainty caused by data limitations and incomplete knowledge. These techniques enable causal inference for time-varying dynamic risk systems. To validate the proposed method, a case study of the positioning system of the first MNPP in China is conducted. The dynamic fatigue failure probability of hinge joints is calculated using predictive technology based on Bayesian theory. Additionally, diagnostic analysis techniques are employed to accurately identify key events and their underlying causes. The analysis results provide valuable data support for on-site operations and management personnel, aiding in the development of effective risk mitigation measures.

The Operation Dependence of C − N Fatigue for Lithium‐Ion Batteries

AbstractLithium‐ion batteries (LIBs) fatigue in repeated service, and their cycle‐life, in resemblance to most materials subject to cyclic loading, scatters over a broad range. The dependence of critical fatigue parameters on ambient temperature and charging or discharging rate, along with the scattering nature of cycle‐life is of practical significance. Through large‐scale experimental investigations, it is shown how both temperatures and charging‐discharging rates may influence critical parameters in the C − N fatigue dependence for LIBs. The cycle‐life N of a battery subject to an average charging rate C follows C = c0 (T)Nb(D), where c0 varies with temperature T and b is a function of the discharging rate D. It is further shown that the cycle‐life of LIBs follows a lognormal distribution. The revealed cycle‐life distribution of LIBs and their fatigue law enable the construction of a probabilistic C − N model, which can be used to quantify the fatigue failure probability in LIBs. Results reported here are of compelling importance for the life‐span evaluation and safety design of large‐scale battery packing in electric vehicles and energy storage where tens of hundreds of batteries working in concert is desired.

Fatigue-life and stress distribution of a glass-ceramic under different loading conditions

This study aimed to evaluate the effect of different loading conditions on the mechanical behavior and stress distribution of a leucite-reinforced glass-ceramic. Plate-shaped ceramic specimens were obtained from leucite-reinforced glass-ceramic (1.5 × 8.4 × 8.3 mm) and adhesively cemented to a dentin analog substrate. Monotonic and cyclic contact fatigue tests were performed to simulate sphere-to-flat contact, using a 6 mm diameter spherical piston; and flat-to-flat contact, using a 3 mm diameter flat piston. For the monotonic test (n=20), a gradual compressive load (0.5 mm/min) was applied to the specimen using a universal testing machine. Failure load data were analyzed with Weibull statistics. The cyclic contact fatigue test was performed using protocols (load and a number of cycles) defined by the boundary technique (n=30). Fatigue data were analyzed using an inverse power law relationship and Weibull-lifetime distribution. The stress distribution was investigated using Finite Element Analysis (FEA). The monotonic and the fatigue Weibull modulus were similar among the two contact conditions. In fatigue, the slow crack growth exponent was greater for sphere-to-flat contact, which indicates that the load level had a greater effect on the specimen's probability of failure. In conclusion, FEA showed different stress distribution for the tested loading conditions. The stress distribution and probability of fatigue failure of specimens tested in sphere-to-flat contact showed greater dependency to load level.

A study of the interaction between depression internal solitary waves and submerged floating tunnels in stratified fluids

Internal wave is a wave phenomenon that traverses almost the depth of the ocean, posing safety challenges and threats to any underwater structure, including the submerged floating tunnel (SFT). Based on the Navier-Stokes (NS) equations and the density transport equation, a numerical model for analyzing the nonlinear interaction between depression internal solitary waves (ISWs) and SFTs in stratified fluids is proposed. After verification with theoretical and experimental results, the hydrodynamic model is utilized to simulate the prototype ISW–SFT interaction under three conditions, i.e., fixed SFT, unconstrained SFT, and constrained SFT. The variation of wave and velocity fields around the SFT in the interaction is simulated, which leads to the wave forces on the SFT as well as the induced motion responses. The simulated results show the wave forces, including horizontal force, vertical force, and moment, are closely correlated with the submergence, wave amplitude and density ratio. When the SFT is unconstrained, the sway and heave displacements are extremely large, while the roll angle is relatively small. When the SFT is constrained by mooring line, it is found the motion displacements are much smaller than the unconstrained condition, inferring the high efficiency of mooring line constraint. However, as motion displacements in the constrained condition are close to the allowable deflection limit of floating bridges for reliability and serviceability, the motion responses should be carefully considered, especially when the SFT encounters internal soliton trains, which may lead to the probability of fatigue failure.

16 - Fatigue failure probability of CAD/CAM machined lithium silicate glass-ceramic

16 - Fatigue failure probability of CAD/CAM machined lithium silicate glass-ceramic

Assessing Fatigue Failure Probability for Structural Components under Deterministic and Stochastic Loading Taking into Account the Initial Crack Size Scatter

Assessing Fatigue Failure Probability for Structural Components under Deterministic and Stochastic Loading Taking into Account the Initial Crack Size Scatter

Probability Prediction Approach of Fatigue Failure for the Subsea Wellhead Using Bayesian Regularization Artificial Neural Network

The subsea wellhead (SW) system is a crucial connection between blowout preventors (BOPs) and subsea oil and gas wells. Excited by cyclical fatigue dynamic loadings, the SW is prone to fatigue failure, which would lead to the loss of well integrity and catastrophic accidents. Based on the Bayesian Regularization Artificial Neuron Network (BRANN), this paper proposes an efficient probability approach to predict the fatigue failure probability of SW during its entire life. In the proposed method, the BRANN fatigue damage (BRANN-FD) model reflecting the non-linear relationship between the input and output data was developed by the limited fatigue damage analysis data, which was utilized to generate thousands of non-numerical fatigue damage data of SW rapidly. Combining parametric and non-parametric estimation methods, the probability density function (PDF) of SW fatigue damage was determined to calculate the accumulation fatigue damage during service life. Using the logistic regression, the fatigue failure probability of SW was predicted. The application of the proposed approach was demonstrated by a case study. The results illustrated that the fatigue damage of SW would be viewed as obeying the Lognormal distribution, which could be used to obtain the accumulation fatigue damage in operation conveniently. Furthermore, the fatigue failure probability of SW nonlinearly increased with the increment in the accumulation fatigue damage of SW, which could be helpful to ensure the operation safety of SW in deepwater oil and gas development, especially for aged wellhead.

Flexure fatigue strength and failure probability of self compacting concrete made with RCA and blended cements

The examination of the fatigue strength of self-compacting concrete (SCC) under flexural loads is the prime objective of the present investigation. SCC comprises recycled concrete aggregates (RCA) and blended cements, i.e., either silica fume (SF) or metakaolin (MK), which differentiates it from the reference mix. Fatigue life data has been accumulated by conducting flexural fatigue tests on beam specimens of size 100 mm × 100 mm × 500 mm. The existing fatigue equations based on SN relationships have been tested with the experimental data and proposed to predict the flexural fatigue strength for SCC constituting RCA and blended cements. An approach based on failure probability (Pf) has also been adopted for the prediction of flexural fatigue strength of SCC mixes. Therefore, SNPf curves have been generated for various SCC mixes under consideration. Several mathematical equations have also been proposed for various SCC mixes to predict the flexure fatigue strength at the desired level of failure probability (Pf). Endurance limit has also been evaluated for various SCC mixes. The introduction of blended cements in SCC mixes constituting RCA enhances the endurance limit, but the enhancement of endurance limit is most prominent in SCC mix containing RCA and MK.

Risk-based inspection planning for internal surface defected oil pipelines exposed to fatigue

The pipelines should be inspected periodically to minimize the probability of failure. The timing of these periodic inspections can be determined by a risk assessment that considers the probability of failure of the pipeline and the potential undesirable effects of the chemical transferred. These are referred to as risk-based inspection methods in the literature. We evaluate pipelines with internal surface defects and under the effect of variable pressure in this study. Internal surface defects were considered production-related, and there is a lack of studies in the literature examining such kinds of defects. These defects generate the concentration of stress on the pipeline section. The reliability variation of the pipeline depends on some factors, namely, surface defect geometry, the pipe section geometry, and the mechanical properties of the pipe. Fatigue is accepted as the crucial damage mechanism. The cause of variable load is considered as the number of operations. As a result of the study, the probability of fatigue failure for pipelines with internal surface defects was evaluated. Then, a new approach was proposed for risk-based inspection planning of the pipelines. As a case study, the probability of fatigue failure on a fuel pipeline established between a refinery and an oil terminal operating in Turkey was calculated. Also, we tried to determine the most appropriate inspection time. The data used in the planning phase was collected from a measurement process performed on the pipeline using PIG (Pipe Inspection Gauge). Finite element analysis was used to calculate stress concentration factors. Finally, we used the Monte Carlo simulation method to obtain the probability of failure.

Prediction of flexural fatigue life and failure probability of normal weight concrete

Fatigue life has to be considered in the design of many concrete structures at various stress levels and stress ratios. Many flexural fatigue test results of plain normal-weight concrete are available in the literature and almost every set of test results provides different fatigue equations. It is necessary, though, to have a common fatigue equation to predict the design fatigue life of concrete structures under flexural load with reasonable accuracy. Therefore, a database of flexural fatigue test results was created for concrete with strengths ranging from 25 to 65 MPa; this database was used to derive new fatigue equations (Wöhler fatigue equation and S-N power relationship) for predicting the flexural fatigue life of normal-weight concrete. The concept of equivalent fatigue life was introduced to obtain a fatigue equation using the same stress ratio. A probabilistic analysis was also carried out to develop flexural fatigue equations that incorporate failure probabilities.

A probabilistic approach to the description of the crack kinetics and fatigue failure of structural components with allowance for the effect of crack retardation after overloads

A probabilistic approach to assessing fatigue strength and cyclic life based on Wheeler’s model is presented. The approach provides the possibility of estimating the probability of failure of the structural component under consideration, with allowance for the effect of crack retardation after the overload. The initial crack size, the parameters of the Paris equation, and the exponent of the crack retardation power function are assumed random parameters. The kinetics of a fatigue crack is described by cycle-by-cycle integration of the Paris equation written in finite differences. The probabilistic estimate is performed using the Monte Carlo statistical simulation method by repeatedly solving the Cauchy problem for various combinations of the values of random model parameters that are generated under the adopted laws of the probability distributions of those parameters. The probabilistic distributions of random parameters are selected proceeding from the level of the uncertainty of the problem using available statistical data without taking into accounting for data on the actual state of a particular structural component in real operation conditions. A computer code in Matlab environment has been developed for statistical description of the fatigue crack growth in structural components which allows accounting for the effect of crack retardation. An example of assessing the probability of the fatigue failure of a pipeline component with a longitudinal crack on the inner surface loaded by the internal pressure which varies according to a periodic law with a constant amplitude is presented for single tensile overload of the pipeline component. The dependencies of the fatigue failure probability on the number of loading cycles for various combinations of model parameters were obtained. The sensitivity of the fatigue failure probability under a given number of loading cycles to changes of the values of structural parameters was assessed.

Fatigue Life Scatter Modeling and Failure Probability Prediction of Multisite Damage Structural Part under Variable Amplitude Load History

An “index stress” (a constant amplitude cyclic stress) is introduced to characterize the scatter of the fatigue life under a variable amplitude load history. It is defined as a fatigue damage weighted average of the amplitudes of the individual stress cycles in a variable amplitude load history. The fatigue test results show that the scatter of fatigue life under variable amplitude load histories can be well characterized by such defined index stress. For probabilistic fatigue life prediction and failure probability estimation, a multiple-site damage structural part is taken as a series system, and the total probability theorem is applied to reflect the effect of load history uncertainty on failure probability.

A dynamic criterion for failure probability prediction of GFRP laminates using Lamb wave velocity with improved accuracy and consistency

This paper presents a dynamic criterion for improving the performance on fatigue life and failure probability prediction of GFRP laminates using Lamb wave velocity. A statistic damage model is built upon a Lamb wave velocity degradation model by introducing a normal distributed random variable to represent the uncertainties. The posterior distributions of the model parameters are obtained by Bayesian inference and Metropolis-Hastings algorithm with noninformative and informative prior distributions. Most importantly, a dynamic criterion is proposed to obtain a specimen-specific failure threshold value based on an assumption that the critical damage stage (CDS) of each specimen differs due to the randomicity of initial flaws. Experimental validation is conducted to compare the predicted fatigue life with both the dynamic criterion and traditional static criterion. The results show that applications of the dynamic criterion can improve the predicting accuracy and consistency, especially during early stages when available data is limited.