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

Seghier, Mohamed El Amine Ben; Carvalho, Hermes; Keshtegar, Behrooz; Correia, José A.F.O.; Berto, Filippo

doi:10.1111/ffe.13325

Cited by 38 publications

(14 citation statements)

References 46 publications

(46 reference statements)

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“…To get the accurate stress intensity factor (SIF) along the crack path, some numerical simulation methods are available in the previous works 40,41 . The contour integration method was utilized in this paper for the finite element simulation via the commercial software ABAQUS.…”

Section: Resultsmentioning

confidence: 99%

Very high cycle fatigue behaviors of GH4169 superalloy at room and high temperatures

Zhang

Teng

Wang

et al. 2022

Fatigue Fract Eng Mat Struct

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The fatigue behavior of a Ni‐based superalloy of grade GH4169 has been investigated at room temperature and high temperature (RT and HT) up to a very high cycle regime using ultrasonic and electromagnetic fatigue systems. The experiments reveal that mixed‐mode cracking occurred in the first crack growth stage at HT. It was followed by a horizontal crack propagation stage regardless of the initial orientation of the crack. Complex pod‐like structures occur in the grain after being loaded by a large number of cycles at RT. These structures are induced by the interaction of dislocation movement with γ″ precipitates. With the help of transmission electron microscopy investigations, it could be shown that shear bands indicate that the sliding movement dominates the superalloy's microstructure variation.

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Section: Resultsmentioning

confidence: 99%

Very high cycle fatigue behaviors of GH4169 superalloy at room and high temperatures

Zhang

Teng

Wang

et al. 2022

Fatigue Fract Eng Mat Struct

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“…where N denotes the number of fatigue cycles with stable crack growth; while C and m are material constants. The expression of the stress intensity factor for mode I (opening mode) can be given as function of the nominal stress (σ), the crack size (a), and the correction factor (F) as follows [45]:…”

Section: Pitting Corrosion Modelmentioning

confidence: 99%

Time-Dependent Reliability Analysis of Reinforced Concrete Beams Subjected to Uniform and Pitting Corrosion and Brittle Fracture

2021

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Reinforced concrete (RC) beams are basic elements used in the construction of various structures and infrastructural systems. When exposed to harsh environmental conditions, the integrity of RC beams could be compromised as a result of various deterioration mechanisms. One of the most common deterioration mechanisms is the formation of different types of corrosion in the steel reinforcements of the beams, which could impact the overall reliability of the beam. Existing classical reliability analysis methods have shown unstable results when used for the assessment of highly nonlinear problems, such as corroded RC beams. To that end, the main purpose of this paper is to explore the use of a structural reliability method for the multi-state assessment of corroded RC beams. To do so, an improved reliability method, namely the three-term conjugate map (TCM) based on the first order reliability method (FORM), is used. The application of the TCM method to identify the multi-state failure of RC beams is validated against various well-known structural reliability-based FORM formulations. The limit state function (LSF) for corroded RC beams is formulated in accordance with two corrosion types, namely uniform and pitting corrosion, and with consideration of brittle fracture due to the pit-to-crack transition probability. The time-dependent reliability analyses conducted in this study are also used to assess the influence of various parameters on the resulting failure probability of the corroded beams. The results show that the nominal bar diameter, corrosion initiation rate, and the external loads have an important influence on the safety of these structures. In addition, the proposed method is shown to outperform other reliability-based FORM formulations in predicting the level of reliability in RC beams.

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“…e flowchart of the GA-ANN method is illustrated in Figure 4. Initially, a feasible NN's topology was predefined through determining the number of neurons in the hidden layer [46,47]. After that, steps to improve the performance of neural networks through GA algorithms begin as follows:…”

Section: Ann-gamentioning

confidence: 99%

Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting

et al. 2020

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The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized support vector regression model with a genetic algorithm (SVR-GA) over the other ML forecasting models for monthly river flow forecasting using 90%–10% data division. In addition, it was found to improve the accuracy in forecasting high flow events. The unique architecture of developed SVR-GA due to the ability of the GA optimizer to tune the internal parameters of the SVR model provides a robust learning process. This has made it more efficient in forecasting stochastic river flow behaviour compared to the other developed hybrid models.

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

Cited by 38 publications

References 46 publications

Very high cycle fatigue behaviors of GH4169 superalloy at room and high temperatures

Very high cycle fatigue behaviors of GH4169 superalloy at room and high temperatures

Time-Dependent Reliability Analysis of Reinforced Concrete Beams Subjected to Uniform and Pitting Corrosion and Brittle Fracture

Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting

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