Optimization of fatigue life of pearlitic Grade 900A steel based on the combination of genetic algorithm and artificial neural network

Reza Masoudi Nejad; Nima Sina; Wenchen Ma; Zhiliang Liu; Filippo Berto; Aboozar Gholami

doi:10.1016/j.ijfatigue.2022.106975

Optimization of fatigue life of pearlitic Grade 900A steel based on the combination of genetic algorithm and artificial neural network

Reza Masoudi Nejad
, Nima Sina
, Wenchen Ma
, Zhiliang Liu
, Filippo Berto
, Aboozar Gholami

Research output: Contribution to journal › Article › peer-review

43 Scopus citations

Abstract

In this paper, the fatigue life of pearlitic Grade 900A steel used in railway applications is investigated. To predict the fatigue life of pearlitic Grade 900A steel based on the number of cycles of the particular stress level in the load block, occurrence ratio and overload ratio, a feed-forward neural network is designed. The results of this artificial neural network are compared to the surface fitting method. Then sensitivity analysis is applied to the obtained artificial neural network values to measure the effect of each input parameter on the fatigue life. Finally, the genetic algorithm is applied to find the maximum fatigue life based on the input values.

Original language	English
Article number	106975
Journal	International Journal of Fatigue
Volume	162
DOIs	https://doi.org/10.1016/j.ijfatigue.2022.106975
State	Published - Sep 2022
Externally published	Yes

Keywords

Artificial neural network
Fatigue life
Genetic algorithm
Railway rail
Stress intensity factor

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10.1016/j.ijfatigue.2022.106975

Cite this

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title = "Optimization of fatigue life of pearlitic Grade 900A steel based on the combination of genetic algorithm and artificial neural network",

abstract = "In this paper, the fatigue life of pearlitic Grade 900A steel used in railway applications is investigated. To predict the fatigue life of pearlitic Grade 900A steel based on the number of cycles of the particular stress level in the load block, occurrence ratio and overload ratio, a feed-forward neural network is designed. The results of this artificial neural network are compared to the surface fitting method. Then sensitivity analysis is applied to the obtained artificial neural network values to measure the effect of each input parameter on the fatigue life. Finally, the genetic algorithm is applied to find the maximum fatigue life based on the input values.",

keywords = "Artificial neural network, Fatigue life, Genetic algorithm, Railway rail, Stress intensity factor",

author = "\{Masoudi Nejad\}, Reza and Nima Sina and Wenchen Ma and Zhiliang Liu and Filippo Berto and Aboozar Gholami",

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year = "2022",

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doi = "10.1016/j.ijfatigue.2022.106975",

language = "英语",

volume = "162",

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T1 - Optimization of fatigue life of pearlitic Grade 900A steel based on the combination of genetic algorithm and artificial neural network

AU - Masoudi Nejad, Reza

AU - Sina, Nima

AU - Ma, Wenchen

AU - Liu, Zhiliang

AU - Berto, Filippo

AU - Gholami, Aboozar

PY - 2022/9

Y1 - 2022/9

N2 - In this paper, the fatigue life of pearlitic Grade 900A steel used in railway applications is investigated. To predict the fatigue life of pearlitic Grade 900A steel based on the number of cycles of the particular stress level in the load block, occurrence ratio and overload ratio, a feed-forward neural network is designed. The results of this artificial neural network are compared to the surface fitting method. Then sensitivity analysis is applied to the obtained artificial neural network values to measure the effect of each input parameter on the fatigue life. Finally, the genetic algorithm is applied to find the maximum fatigue life based on the input values.

AB - In this paper, the fatigue life of pearlitic Grade 900A steel used in railway applications is investigated. To predict the fatigue life of pearlitic Grade 900A steel based on the number of cycles of the particular stress level in the load block, occurrence ratio and overload ratio, a feed-forward neural network is designed. The results of this artificial neural network are compared to the surface fitting method. Then sensitivity analysis is applied to the obtained artificial neural network values to measure the effect of each input parameter on the fatigue life. Finally, the genetic algorithm is applied to find the maximum fatigue life based on the input values.

KW - Artificial neural network

KW - Fatigue life

KW - Genetic algorithm

KW - Railway rail

KW - Stress intensity factor

UR - https://www.scopus.com/pages/publications/85129742494

U2 - 10.1016/j.ijfatigue.2022.106975

DO - 10.1016/j.ijfatigue.2022.106975

M3 - 文章

AN - SCOPUS:85129742494

SN - 0142-1123

VL - 162

JO - International Journal of Fatigue

JF - International Journal of Fatigue

M1 - 106975

ER -

Optimization of fatigue life of pearlitic Grade 900A steel based on the combination of genetic algorithm and artificial neural network

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Keywords

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