Estimation study of structure crack propagation under random load based on multiple factors correction

Zhu, Lin; Jia, Minping

doi:10.1007/s40430-016-0654-z

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…Conventional mechanistic models (Ai et al., 2018; Zhu & Jia, 2017) can predict track degradation from a small amount of geometrical data. These models mostly focus on track and ballast settlement.…”

Section: Literature Reviewmentioning

confidence: 99%

Segment‐condition‐based railway track maintenance schedule optimization

Chang

Liu

Tang

2022

Computer aided Civil Eng

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Reasonable maintenance plans are important for ensuring safe train operation and prolonging the service life of tracks. However, previous studies on the scheduling and optimization of railway track maintenance plans possess the following limitations. First, scheduling optimization models generally operate at the planning level with months as the time units rather than at the operation scheduling level with days as the time units. Second, they fail to consider the bidirectional feedback and dynamic impact between predicted condition and maintenance scheduling. Third, actual on‐site operation strategies (e.g., opportunistic and centralized maintenance) are neglected. To address these issues, this paper proposes a novel operation‐level maintenance scheduling framework based on segment condition considering both opportunistic and centralized maintenance strategies. According to the problem characteristics, ae hybrid multi‐objective optimization algorithm based on a quantum‐behaved particle swarm optimization was designed. Furthermore, by utilizing actual practical cases, the effectiveness of the model and algorithm was verified via comparison with MOPSO, NSGA‐II, NSGA‐III, and Gurobi in different scenarios.

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Section: Literature Reviewmentioning

confidence: 99%

Segment‐condition‐based railway track maintenance schedule optimization

Chang

Liu

Tang

2022

Computer aided Civil Eng

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“…This method effectively shortened the testing time and reduced costs. In a separate study, Lu et al [2] developed a three-tier accelerated load spectrum based on a uniform failure mechanism principle. Utilizing this method, they successfully predicted the fatigue life of a large bogie through accelerated life testing.…”

Section: Introductionmentioning

confidence: 99%

Numerical Analysis of Crack Propagation in an Aluminum Alloy under Random Load Spectra

Wang,

Zheng,

Liu

et al. 2024

Modelling

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This study develops a rapid algorithm coupled with the finite element method to predict the fatigue crack propagation process and select the enhancement factor for the equivalent random load spectrum of accelerated fatigue tests. The proposed algorithm is validated by several fatigue tests of an aluminum alloy under the accelerated random load spectra. In the validation process, two kinds of panels with different geometries and sizes are used to calculate the stress intensity factor, critical crack length, and crack propagation life. The simulated and experimental findings indicate that when the aluminum alloy is in a low plasticity state, the crack propagation life exhibits a linear relationship with the acceleration factor. When the aluminum alloy is in a high plasticity state, this study proposes an empirical formula to calculate the equivalent stress intensity factor and crack propagation life. The normalized empirical formula is independent of the geometry and size of different samples, although the fracture processes are different in the two kinds of panels used in our study. Overall, the numerical method proposed in this paper can be applied to predict the fatigue crack propagation life for the random spectrum of large samples based on the results of the simulated accelerated crack propagation process and the accelerated fatigue tests of small samples to reduce the cost and time of the testing.

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“…Fatigue involves crack initiation, propagation and final fracture. The fatigue cracking problem of mechanical structures and components are exposed to variable amplitude loading (VAL) [ 1 , 2 , 3 , 4 ]. The number of cycles required for crack growth to reach a certain distance or failure can be predicted based on the principle of fracture mechanics.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Fatigue Crack Growth Rate Based on Entropy Generation

Idris

Abdullah

Thamburaja

et al. 2019

Entropy

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This paper presents the assessment of fatigue crack growth rate for dual-phase steel under spectrum loading based on entropy generation. According to the second law of thermodynamics, fatigue crack growth is related to entropy gain because of its irreversibility. In this work, the temperature evolution and crack length were simultaneously measured during fatigue crack growth tests until failure to ensure the validity of the assessment. Results indicated a significant correlation between fatigue crack growth rate and entropy. This relationship is the basis in developing a model that can determine the characteristics of fatigue crack growth rates, particularly under spectrum loading. Predictive results showed that the proposed model can accurately predict the fatigue crack growth rate under spectrum loading in all cases. The root mean square error in all cases is 10−7 m/cycle. In conclusion, entropy generation can accurately predict the fatigue crack growth rate of dual-phase steels under spectrum loading.

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Estimation study of structure crack propagation under random load based on multiple factors correction

Cited by 6 publications

References 26 publications

Segment‐condition‐based railway track maintenance schedule optimization

Segment‐condition‐based railway track maintenance schedule optimization

Numerical Analysis of Crack Propagation in an Aluminum Alloy under Random Load Spectra

Prediction of Fatigue Crack Growth Rate Based on Entropy Generation

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