Fatigue damage prediction of short edge crack under various load: Direct Optimized Probabilistic Calculation

Abstract Fatigue crack propagation depends on a number and value of stress range cycles. This is a time factor in the course of reliability for the entire designed service life. Three sizes are important for the characteristics of the propagation of fatigue cracks - initial size, detectable size and acceptable size. The theoretical model of a fatigue crack progression can be based on a linear elastic fracture mechanics (uses Paris-Erdogan law). Depending on location of an initial crack, the crack may propagate in structural element (e.g. from the edge or from the surface under various load) that could be described by calibration functions. When determining the required degree of reliability, it is possible to specify the time of the first inspection of the construction which will focus on the fatigue damage. Using a conditional probability and Bayesian approach, times for subsequent inspections can be determined based on the results of the previous inspection. For probabilistic modelling of a fatigue crack progression was used the original and a new probabilistic method - the Direct Optimized Probabilistic Calculation ("DOProC"), which uses a purely numerical approach without any simulation techniques or approximation approach based on optimized numerical integration. Compared to conventional simulation techniques is characterized by greater accuracy and efficiency of the computation.

[1]  Zdeněk Kala Influence of partial safety factors on design reliability of steel structures ‐ probability and fuzzy probability assessments , 2007 .

[2]  王茜,et al.  Fatigue Service Life Evaluation of Existing Steel and Concrete Bridges , 2015 .

[3]  Daniel Straub,et al.  Reliability analysis and updating of deteriorating systems with subset simulation , 2017 .

[4]  Natasha Smith,et al.  Bayesian networks for system reliability reassessment , 2001 .

[5]  P. C. Paris,et al.  A Critical Analysis of Crack Propagation Laws , 1963 .

[6]  Martin Krejsa,et al.  Probabilistic prediction of fatigue damage based on linear fracture mechanics , 2016 .

[7]  Yibing Xiang,et al.  Application of inverse first-order reliability method for probabilistic fatigue life prediction , 2011 .

[8]  Juraj Králik Deterministic and Probabilistic Analysis of Steel Frame Bracing System Efficiency , 2013 .

[9]  José A.F.O. Correia,et al.  A probabilistic fatigue approach for riveted joints using Monte Carlo simulation , 2015 .

[10]  Yuqing He,et al.  Rotor-flying manipulator: Modeling, analysis, and control , 2014 .

[11]  Martin Krejsa,et al.  Determination of Inspections of Structures Subject to Fatigue , 2011 .

[12]  Martin Krejsa,et al.  Structural Reliability Analysis Using DOProC Method , 2016 .

[13]  Josef Vičan,et al.  Analysis of Existing Steel Railway Bridges , 2016 .

[14]  X. W. Ye,et al.  A State-of-the-Art Review on Fatigue Life Assessment of Steel Bridges , 2014 .

[15]  Xuefei Guan,et al.  Model selection, updating, and averaging for probabilistic fatigue damage prognosis , 2011 .

[16]  Zdeněk Poruba,et al.  Influence of mean stress and stress amplitude on uniaxial and biaxial ratcheting of ST52 steel and its prediction by the AbdelKarim-Ohno model , 2016 .

[17]  Yang Liu,et al.  Fatigue Reliability Assessment of Welded Steel Bridge Decks under Stochastic Truck Loads via Machine Learning , 2017 .