A nonlinear fatigue damage model: Comparison with experimental damage evolution of S355 (SAE 1020) structural steel and application to offshore jacket structures

Aeran, Ashish; Acosta, Ruth; Siriwardane, Sudath C.; Starke, Peter; Mikkelsen, Ove; Langen, Ivar; Walther, Frank

doi:10.1016/j.ijfatigue.2020.105568

Cited by 19 publications

(10 citation statements)

References 17 publications

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“…Hence, the larger difference of the load levels, the more significant the interaction effect will tend to be. For this reason, Aeran et al 36 further improve the model. The new model is as shown in Equation (3).…”

Section: The Nonlinear Fatigue Damage Accumulation Modelmentioning

confidence: 99%

A modified nonlinear fatigue damage accumulation model for life prediction of rolling bearing under variable loading conditions

Huang

et al. 2022

Fatigue Fract Eng Mat Struct

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During the use of rolling bearings in wind turbines, aero engines, and other equipment, they often operate under variable operating and loading conditions. The practice has shown that the sequence of these loads and their interaction has a significant impact on the fatigue life of rolling bearings. In order to better understand this issue, a modified nonlinear fatigue damage accumulation model, which considers the influences of both load sequence and load interaction, is proposed in this paper and verified using the fatigue testing data for six materials. Then, the verified model was applied to evaluate the fatigue damage accumulation and fatigue life of a rolling bearing. The results have shown that, compared with the existing models, the proposed model can more accurately predict the damage accumulation and fatigue life of the rolling bearing under variable loading conditions.

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Section: The Nonlinear Fatigue Damage Accumulation Modelmentioning

confidence: 99%

A modified nonlinear fatigue damage accumulation model for life prediction of rolling bearing under variable loading conditions

Huang

et al. 2022

Fatigue Fract Eng Mat Struct

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“…With the knowledge of how the virtual PC eigenvectors' elements change for each mutually exclusive damage case in the virtual SHM, as shown in the Finite Element Model Sensitivity Analysis section (6), the combination of damage cases and damage intensities that most resembles the real structure behavior is searched with the objective function (7) to minimize the average NRMSE between the virtual PC eigenvectors' elements and the real SHM PC eigenvectors' elements results (8). However, it is unfeasible to run the FE analysis at the necessary amount of damage and intensity combinations to accurately target the objective function (7). Therefore, a Monte Carlo experiment is used to optimize a quasi-random sequence sampling of damage coefficients ξ as shown in Figure 13.…”

Section: Finite Element Model Updatingmentioning

confidence: 99%

“…Next, the NRMSE between the weighted virtual PCs fb vg k,m N × 1 and the real PC elements fvg k N × 1 for the first to fourth PCs' eigenvectors are calculated (8). Finally, the objective function (7) selects the Sobol sample vector fξg m j × 1 with the damage coefficients resulting in the smaller average of the NRMSE.…”

Section: Finite Element Model Updatingmentioning

confidence: 99%

“…6 In this sense, extensive research has been done on the condition assessment of existing bridges based on field tests and numerical analysis in the last three decades. [7][8][9][10][11][12][13][14][15][16] Their outcomes highlighted how challenging it could be to perform structural health assessment, mainly due to the difficulty of mathematically describing damage and deterioration processes, 17 and the lack of measurements from real-size damaged structures. 18 SHM strategies can be divided into two categories according to the presence or absence of physics-based numerical models: model-based and data-driven methods.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying the extent of local damage of a 60-year-old prestressed concrete bridge: A hybrid SHM approach

Sakiyama

Veríssimo

Lehmann

et al. 2022

Structural Health Monitoring

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The increasing demand for civil infrastructures, the aging of existing assets, and the strengthening of safety and liability laws have led to the inclusion of structural health monitoring (SHM) techniques into the structural management process. With the latest developments in the sensors field and computational power, real-scale SHM systems’ deployment has become logistically and economically feasible. However, it is still challenging to perform a quantitative evaluation of the structural condition based on measured data. The paper addresses recent efforts to associate measured observations with an identification of local stiffness reduction as a global parameter for damage onset and growth. It proposes a hybrid methodology for model updating and damage identification. The proposed methodology is built on data feature extraction using the principal component analysis (PCA), finite element (FE) simulation, and Monte Carlo simulation to quantify the extent of local damage of a 60-year-old prestressed concrete bridge. The methodology allows a sensor-specific quantification of the local stiffness reduction and makes it possible to focus succeeding bridge inspection, recalculation, and repair works on these areas. Even more, the monitoring in combination with the FE model and proposed methodology provides continuous information on developing stiffness reduction and the acuteness of rehabilitation measures.

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“…Barbosa et al [ 14 ] investigated the growth rates of fatigue cracks in EH36 steel caused by submerged arc welding, and found that the threshold value of crack propagation in the fusion zone is higher than that in the base metal, because a large amount of acicular ferrite is formed in this zone, which impedes the crack propagation and can also cause the crack to deflect. Relevant papers related to the fatigue behavior mainly focus on establishing a fatigue damage model and measuring the S-N curves [ 15 , 16 ]. Wang et al [ 17 ] investigated the fatigue strength of EH36 steel with T-shape welded joints at low temperature.…”

Section: Introductionmentioning

confidence: 99%

Fatigue Crack Growth Behavior and Fracture Toughness of EH36 TMCP Steel

Zhu

Zhang²,

Peng

et al. 2021

Materials

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The fatigue crack growth behavior and fracture toughness of EH36 thermo-mechanical control process (TMCP) steel were investigated by fatigue crack growth rate testing and fracture toughness testing at room temperature. Scanning electron microscopy was used to observe the fracture characteristics of fatigue crack propagation and fracture toughness. The results indicated that the microstructure of EH36 steel is composed of ferrite and pearlite with a small amount of texture. The Paris formula was obtained based on the experimental data, and the value of fracture toughness for EH36 steel was also calculated using the J-integral method. The observations conducted on fatigue fracture surfaces showed that there were a lot of striations, secondary cracks and tearing ridges in the fatigue crack propagation region. Additionally, there existed many dimples on the fracture surfaces of the fracture toughness specimens, which indicated that the crack was propagated through the mechanism of micro-void growth/coalescence. Based on the micromechanical model, the relationship between the micro-fracture surface morphology and the fracture toughness of EH36 steel was established.

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A nonlinear fatigue damage model: Comparison with experimental damage evolution of S355 (SAE 1020) structural steel and application to offshore jacket structures

Cited by 19 publications

References 17 publications

A modified nonlinear fatigue damage accumulation model for life prediction of rolling bearing under variable loading conditions

A modified nonlinear fatigue damage accumulation model for life prediction of rolling bearing under variable loading conditions

Quantifying the extent of local damage of a 60-year-old prestressed concrete bridge: A hybrid SHM approach

Fatigue Crack Growth Behavior and Fracture Toughness of EH36 TMCP Steel

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