Prediction Of Stress Intensity Factors Research Articles

A novel correlation models for predicting stress intensity factor of semi-elliptical crack in welded pipelines under cyclic internal pressure based on Bézier base XIGA

Pipelines are strategic infrastructures that are used for energy transfer in the world. Cracks in the residual stress field are the common defects in welded pipelines. These defects with cyclic internal pressure cause crack growth and decrease the fatigue life of pipelines. In this study, a novel correlation model is proposed to predict the stress intensity factor (SIF) of the semi-elliptical crack front in welded steel pipeline subjected to cyclic internal pressure. For this purpose, the Bézier extraction base extended isogeometric method is applied for numerical analysis and preparing a database of results based on the thermo elastic-plastic equation. Taguchi standard L25 orthogonal array was used as the analysis design to create the simulations. New correlations were proposed for SIF prediction using the multivariate linear regression method. These correlations were obtained based on a parametric study that included three parameters. These parameters are dimensionless crack geometry (a/c), pipe geometry (D/t), and loading (P/Pb) or (σres/E). The R-Square (R2) values of the presented model for the welding residual stress and cyclic internal pressure were obtained as 0.989 and 0.992, respectively. The results of FEM analysis with similar conditions were compared to the results of XIGA and correlation models to validate the presented method. The maximum difference between them is approximately 3.71% for the cyclic internal pressure, and 2.94% for the welding residual stress. RMSD for the cyclic internal pressure model is 0.49, and for the welding residual stress model is 0.51. Therefore, the proposed correlation models have acceptable accuracy in predicting the SIF and fatigue crack propagation rate of the semi-elliptical crack in the steel pipes.

Stress intensity factor prediction on offshore pipelines using surrogate modeling techniques

This study aims to predict the accurate stress intensity factor (SIF) of a crack propagating in offshore piping, which is one of the crucial factors used to assess the remaining fatigue life (RFL) of offshore pipelines. Four soft computing techniques are examined and evaluated in the modeling of SIF of a crack propagating in topside piping, as an inexpensive alternative to the finite element methods (FEM). In the training and testing stages, developed models of Functional Network (FN), Emotional Neural Network (ENN), Relevance Vector Machine (RVM), and minimax probability machine regression (MPMR) for SIF prediction were used and compared. Also, a comparative study was conducted for the developed models with the Adaptive Gaussian Process Regression Model (AGPRM). The load, crack depth, and half crack length have been adopted as input variables of the models, and the output variable is the SIF. All variables were simulated and determined based on the flat-plate FEM model. The analysis confirms that the RVM and MPMR models are superior to FN and ENN models for SIF prediction in the training and testing stages. In addition, the RVM and MPMR models show better than AGPRM for the prediction of pipe SIF in the testing stage. The MPMR accurately outperforms all models within 1.31% prediction error, and the majority of its error values at 99% confidence level fall within ±29.64 MPamm.

A Numerical Investigation of an Abnormal Phenomenon of Stress Intensity Factor (SIF) in a Cracked T-Butt Joint Accounting for Welding Effect

Industry design standards such as BS 7910 deployed some empirical formulas for the prediction of stress intensity factor (SIF) based on simulation results from traditional finite element method (FEM). However, such FEM simulation occasionally failed to convince people due to the large discrepancies compared with engineering practice. As a consequence, inaccuracy predictions via such formulas in engineering standards inevitably occur, which will compromise the safety of structures. In our previous research work, an abnormal phenomenon of SIF in a cracked T-butt joint accounting for welding effect has been observed. Compared with BS 7910, the calculation results of SIF at the surface points of welded specimens cannot be well predicted, with a large discrepancy appearing. In order to explore such problem with an abnormal increase at the surface points of cracked welded specimens, a numerical investigation in terms of SIF among BS 7910, XFEM, and FEM is performed in this paper. Numerical models on both a simple cracked plate without welding effect and a cracked T-butt joint with welding effect are developed through ABAQUS. Parametric studies in terms of the effects of varied crack depth to thickness ratio (a/T) and the effects of crack depth to crack half-length ratio (a/c) are carried out. Empirical solutions from BS 7910 are used for comparison. It is found that the XFEM can provide predictions of SIF at both the crack deepest point and crack surface point of a simple cracked plate as accurate as FEM. For a T-butt joint with a transverse stiffener, a large discrepancy in terms of the weld magnification factors (Mk) occurs at the crack surface point compared with empirical predictions. An exceptional increase of von Mises stress gradient in regions close to the weld-toe is found through the simulation of FEM, whereas a constant stress gradient is obtained through XFEM. The comparison results indicate an inappropriate prediction of SIF by the utilization of the empirical formulas in BS 7910. A more reasonable prediction of the SIF at the surface point of a crack is obtained by the XFEM. Therefore, further updating of the empirical solutions in BS 7910 for SIF accounting for welding effect is recommended.

Novel hybridized adaptive neuro‐fuzzy inference system models based particle swarm optimization and genetic algorithms for accurate prediction of stress intensity factor

AbstractThe aim of this study is to develop a new framework for the prediction of stress intensity factor (SIF) using newly developed hybrid artificial intelligence (AI) models. To do so, an adaptive neuro‐fuzzy inference system optimized by two meta‐heuristic algorithms as genetic algorithm (ANFIS‐GA) and particle swarm optimization (ANFIS‐PSO) is proposed. Moreover, a database composed of 150 SIF values obtained using the finite element method (FEM) calculations is used for training and validating the two proposed AI models. The efficiency and accuracy of the proposed AI models were investigated through several assessment criteria. Results showed the outperformance of the ANFIS‐PSO model for accurate prediction of SIF values with R2 = 0.9913, root mean square error (RMSE) = 23.6 and mean absolute error (MAE) = 18.07, whereas both AI models indicate a robust performance in the presence of input variability. Overall, the performed study provides a hybrid AI framework that can serve as an efficient numerical tool for SIF prediction and analysis.

Prediction of Tubular T/Y-Joint SIF by GA-BP Neural Network

In fracture mechanics, the fatigue evaluation depends on the precise provisions of the stress intensity factor (SIF), which is a quantitative factor established to evaluate the effect of stresses along the crack front. Stress intensity factor performs a definitive role in reflecting material fracture and estimating fatigue life of tubular joints. Some researchers have proposed a series of parametric equations to predict SIF values; however, it gave arguably inaccurate results. There is still a need to improve the accuracy of the SIF prediction’s equation. Thus, to overcome this shortage, this study introduced back-propagation (BP) neural network with conjunction to genetic algorithm (GA) (GA-BP) in predicting the SIF of cracked tubular T/Y-joint. To train and verify the results of the hybrid algorithm GA-BP SIFs’ database covers a wide variety of cracked tubular T/Y joints were simulated by ABAQUS and verified against experimental results. Meanwhile, the SIFs’ result produced by the GA-BP optimization method were compared with those SIF calculated from the accessible parametric equations. The comparison indicated that the GA-BP neural network optimization method is reliable, precise and capable tool in calculating the SIFs of cracked T/Y joints and it also provides higher accuracy than common methods.

Statistical distributions for prediction of stress intensity factor using the bootstrap S-version finite element model

Stress intensity factor (SIF) is one of the most fundamental and useful parameters in all of fracture mechanics. The SIF describes the stress state at a crack tip, is related to the rate of crack growth, and used to establish failure criteria due to fracture. The SIF is determined to define whether the crack will grow or not. The aims of this paper is to examine the best sampling statistical distributions in SIF analysis along the crack front of a structure. Box-Muller transformation is used to generate the statistical distributions which is in normal and lognormal distributions. This method transformed from the random number of the variables within range zero and one. The SIFs are computed using the virtual crack-closure method (VCCM) in bootstrap S-version finite element model (BootstrapS-FEM). The normal and lognormal distributions are represented in 95% of confidence bounds from the one hundred of random samples. The prediction of SIFs are verified with Newman-Raju solution and deterministic S-FEM in 95% of confidence bounds. The prediction of SIFs by BootstrapS-FEM in different statistical distribution are accepted because of the Newman-Raju solution is located in between the 95% confidence bounds. Thus, the lognormal distribution for SIFs prediction is more acceptable between normal distributions.

Analysis of stress intensity factor for fatigue crack using bootstrap S-version finite element model

PurposeFailure of the materials occurs once the stress intensity factor (SIF) overtakes the material fracture toughness. At this level, the crack will grow rapidly resulting in unstable crack growth until a complete fracture happens. The SIF calculation of the materials can be conducted by experimental, theoretical and numerical techniques. Prediction of SIF is crucial to ensure safety life from the material failure. The aim of the simulation study is to evaluate the accuracy of SIF prediction using finite element analysis.Design/methodology/approachThe bootstrap resampling method is employed in S-version finite element model (S-FEM) to generate the random variables in this simulation analysis. The SIF analysis studies are promoted by bootstrap S-version Finite Element Model (BootstrapS-FEM). Virtual crack closure-integral method (VCCM) is an important concept to compute the energy release rate and SIF. The semielliptical crack shape is applied with different crack shape aspect ratio in this simulation analysis. The BootstrapS-FEM produces the prediction of SIFs for tension model.FindingsThe mean of BootstrapS-FEM is calculated from 100 samples by the resampling method. The bounds are computed based on the lower and upper bounds of the hundred samples of BootstrapS-FEM. The prediction of SIFs is validated with Newman–Raju solution and deterministic S-FEM within 95 percent confidence bounds. All possible values of SIF estimation by BootstrapS-FEM are plotted in a graph. The mean of the BootstrapS-FEM is referred to as point estimation. The Newman–Raju solution and deterministic S-FEM values are within the 95 percent confidence bounds. Thus, the BootstrapS-FEM is considered valid for the prediction with less than 6 percent of percentage error.Originality/valueThe bootstrap resampling method is employed in S-FEM to generate the random variables in this simulation analysis.

Effect of aspect ratio on dynamic fracture toughness of particulate polymer composite using artificial neural network

The present study discusses about the effect of the aspect ratio of the fillers on the fracture toughness of the glass-filled epoxy composites under impact loading. Three different kinds of fillers (spheres, flakes and rods) were used with different volume fractions (5%, 10% and 15%). Experimental results for Stress Intensity Factor (SIF) were obtained using a gas gun setup and a high speed camera. Further experimental investigation was done using fractographs obtained from Scanning Electron Microscope (SEM). Then the potential of using Artificial Neural Network (ANN) in predicting the effect of filler shape on the fracture behavior is studied. The framework of Multi-Layer Perceptron (MLP) feed forward network was used to predict the SIF history using four input parameters viz. time, dynamic elastic modulus, aspect ratio and volume fraction of the glass fillers. Experimental results of fracture test under impact loading were fed to train the ANN network and later the predicted results were compared with the experimental ones. Owing to the fact that predicted values had an accuracy of 91%, crack initiation toughness was predicted corresponding to the intermediate values of aspect ratio for which the experiments were not performed. Among the four input parameters, aspect ratio (largest/shortest dimension) was found to be the most important parameter in the prediction of SIF after time, followed by the dynamic modulus and volume fraction. The significance of aspect ratio lies in increasing the surface area to volume ratio which is responsible for the interfacial strength between the matrix and the filler and hence affects the fracture toughness of the overall composite material.

Relevance vector based approach for the prediction of stress intensity factor for the pipe with circumferential crack under cyclic loading

Structural integrity assessment of piping components is of paramount important for remaining life prediction, residual strength evaluation and for in-service inspection planning. For accurate prediction of these, a reliable fracture parameter is essential. One of the fracture parameters is stress intensity factor (SIF), which is generally preferred for high strength materials, can be evaluated by using linear elastic fracture mechanics principles. To employ available analytical and numerical procedures for fracture analysis of piping components, it takes considerable amount of time and effort. In view of this, an alternative approach to analytical and finite element analysis, a model based on relevance vector machine (RVM) is developed to predict SIF of part through crack of a piping component under fatigue loading. RVM is based on probabilistic approach and regression and it is established based on Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Model for SIF prediction is developed by using MATLAB software wherein 70% of the data has been used for the development of RVM model and rest of the data is used for validation. The predicted SIF is found to be in good agreement with the corresponding analytical solution, and can be used for damage tolerant analysis of structural components.

Predicting Stress Intensity Factor (KI) of Single Edge Crack Using Displacement Extrapolation Method

Cracks that occur in the structure will be very dangerous if left alone. Cracks can propagate and will eventually cause the structure to fail. Cracks that occur will cause singularity at the end of the crack, and the stress level is very high or reaches without limit (infinity). Stress intensity factor (SIF) is used to determine behavior of structures with crack. One of method that can predict SIF is displacement extrapolation method. Displacement extrapolation methods predict SIF by nodal displacement near end of crack. This research is to predict SIF of a thin plate with a single edge crack of 1.27 mm. The crack mode used in this research is mode I (opening mode) with a tension stress of 60 MPa. Nodal displacement for calculating SIF is taken from result of analyzing the cracked model using Patran / nastran. SIF prediction result from displacement extrapolation then to be compared with the results of analytical calculation.

Experimental Validation of the Adaptive Gaussian Process Regression Model Used for Prediction of Stress Intensity Factor as an Alternative to Finite Element Method

Currently, in the oil and gas industry, finite element method (FEM)-based commercial software (such as ANSYS and abaqus) is commonly employed for determining the stress intensity factor (SIF). In their earlier work, the authors proposed an adaptive Gaussian process regression model (AGPRM) for the SIF prediction of a crack propagating in topside piping, as an inexpensive alternative to FEM. This paper is the continuation of the earlier work, as it focuses on the experimental validation of the proposed AGPRM. For validation purposes, the values of SIF obtained from experiments available in the literature are used. The experimental validation of AGPRM also consists of the comparison of the prediction accuracy of AGPRM and FEM relative to the experimentally derived SIF values. Five metrics, namely, root-mean-square error (RMSE), average absolute error (AAE), mean absolute percentage error (MAPE), maximum absolute error (MAE), and coefficient of determination (R2), are used to compare the accuracy. A case study illustrating the development and experimental validation of the AGPRM is presented. Results indicate that the prediction accuracy of AGPRM is comparable with and even higher than FEM, provided the training points of AGPRM are chosen aptly. Good prediction accuracy coupled with less time consumption favors AGPRM as an alternative to FEM for SIF prediction.

Adaptive Gaussian process regression as an alternative to FEM for prediction of stress intensity factor to assess fatigue degradation in offshore pipeline

The linear elastic fracture mechanics (LEFM) based remnant fatigue life (RFL) assessment of offshore pipeline is used to determine the inspection interval frequency of the aforementioned asset. One of the vital factors of the LEFM approach that determines the accuracy of the RFL estimate (and in turn of the inspection interval frequency) is the Stress Intensity Factor (SIF), which must be evaluated as accurately as possible. For simple crack geometries numerous closed-form equations available in various handbooks and industrial standards provide accurate SIF results. However, it is a common industry practice to utilize finite element method (FEM) for evaluating the SIF for the intricate crack geometries and the complex loading conditions. Although, FEM is known for its accurate SIF calculation, but due to its high computational expense and time-consumption, cycle-by-cycle SIF evaluation (required for the LEFM based RFL assessment) makes the aforementioned method quite laborious. Furthermore, using FEM to evaluate SIF for thousands of pipeline location (undergoing fatigue degradation) on an offshore platform seems to be impractical. Thus, in this manuscript authors have proposed a computationally inexpensive adaptive Gaussian process regression model (AGPRM) which may be utilized as an alternative to FEM for prediction of SIF to assess fatigue degradation in offshore pipeline. The training and testing data for AGPRM consists of 105 and 50 data points (load (L), crack depth (a), half-crack length (c) and SIF values), respectively. Latin Hypercube Sampling (LHS) is used to generate (L, a and c) values while SIF values are evaluated using FEM by carefully accounting for the discretization error emanating due to the finite mesh size in the FEM simulation. After the GPRM has been adaptively trained, it is used to predict the response of the 50 data points. On comparing the values of the SIF (obtained by AGPRM) with the SIF values obtained from FEM, the average residual percentage between the two is found to be 1.76%, thus indicating a good agreement between the AGPRM and FEM model. Furthermore, the time required to predict the SIF of 50 test points is reduced from 50 min (for FEM) to 12 s with the help of the proposed AGPRM, thus making RFL assessment less laborious and time consuming.

A simplified fatigue assessment method for transverse fillet welded joints

Under the as-welded condition the fatigue crack initiation period was considered non-existent and Linear Elastic Fracture Mechanics (LEFM) was used to calculate fatigue strength for a range of weld geometries. Fracture mechanics assessment of welded joints requires accurate solutions for stress intensity factor (SIF). However, the solutions for the SIF of complex welded joints are difficult to determine due to the complicated correction factors. Three methods for SIF prediction are discussed on fillet welded specimens containing continuous or semi-elliptical surface cracks, including the traditional correction method Mk, the approximate correction method Kt, and the suggested additional crack size method (ac+ae). The new additional crack parameter ae is used to replace the stress concentration effect of weld profile Mk, which simplifies the calculation process. Experimental results are collected to support fatigue strength assessment of the additional crack size method.

Neural Networks Modeling of Stress Growth in Asphalt Overlays due to Load and Thermal Effects during Reflection Cracking

Although several techniques have been introduced to reduce reflective cracking, one of the primary forms of distress in hot-mix asphalt (HMA) overlays of flexible and rigid pavements, the underlying mechanism and causes of reflective cracking are not yet well under- stood. Fracture mechanics is used to understand the stable and progressive crack growth that often occurs in engineering components under varying applied stress. The stress intensity factor (SIF) is its basis and describes the stress state at the crack tip. This can be used with the appropriate material properties to calculate the rate at which the crack will propagate in a linear elastic manner. Unfortunately, the SIF is difficult to compute or measure, particularly if the crack is situated in a complex three-dimensional (3D) geometry or subjected to a non- simple stress state. In this study, the neural networks (NN) methodology is successfully used to model the SIF as cracks grow upward through a HMA overlay as a result of both load and thermal effects with and without reinforcing interlayers. Nearly 100,000 runs of a finite-element program were conducted to calculate the SIFs at the tip of the reflection crack for a wide variety of crack lengths and pavement structures. The coefficient of determination (R 2 ) of all the developed NN models except one was above 0.99. Owing to the rapid prediction of SIFs using developed NN models, the overall computer run time for a 20-year reflection cracking prediction of a typical overlay was significantly reduced. DOI: 10.1061/(ASCE)MT.1943-5533.0000153. © 2011 American Society of Civil Engineers. CE Database subject headings: Cracking; Neural networks; Predictions; Models; Finite element method; Thermal factors. Author keywords: Reflective cracking; HMA overlay; Neural networks; Prediction model; Fracture mechanics; Finite element.

균열전파해석에 의한 선체의 피로수명 평가법 -응력강도계수의 간이추정법-

선체에 존재하는 균열의 전파거동을 해석하기 위해서는 응력강도계수의 추정이 그 전제조건이 되지만 현재까지 이러한 복잡 구조물에서 응력강도계수를 간편하게 계산하는 방법이 확립되어 있지 못하다는 점이 가장 큰 걸림돌이 되고 있다. 본 연구에서는 선체와 같이 부정정도가 매우 큰 복잡 구조물에서의 균열전파 거동을 추정하기 위한 전 단계로서 다양한 형태의 균열에 대한 응력강도계수를 용이하게 계산하기 위하여 무균열 상태에서의 응력해석 결과에 기초한 응력강도계수의 간이 추정법을 유도하고, 다른 연구자들의 실험 및 해석 결과와 비교하여 제안된 방법의 유용성을 검증하였다. The prediction of stress intensity factor(SIF) is one of the most important factors to analyse the propagation behavior of cracks in hull structural members. Up to now, however, simplified prediction method of SIF has not yet been established for the cracks experienced in large complex structures. As a first step to predict crack propagation behavior in a ship structure with very large structural redundancies, simplified SIF prediction formulas for various crack shapes were derived based on the results of the stress analysis under a non-crack condition in this study. The adequacy of the proposed method was then verified in comparison with other experimental and analysis results.