Fatigue damage effect approach by artificial neural network

Jimenez-Martinez, Moises; Alfaro-Ponce, Mariel

doi:10.1016/j.ijfatigue.2019.02.043

Cited by 50 publications

(28 citation statements)

References 25 publications

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“…Benchmarking Marquardt and Zenner, 27 Mathur et al, 29 Liao et al, 31 Artymiak et al 51 and Kim et al 133 Case study Yang et al, 1 Al Assaf and El Kadi, 12 Bezazi et al, 13 Salmalian et al, 14 Figueira Pujol and Andrade Pinto, 15 Salmalian et al, 16 Rohman et al, 18 Kong et al, 19 Han, 20 Aymerich and Serra, 21 Venkatesh and Rack, 22 Pleune and Chopra, 23 Sohn and Bae, 24 Genel, 25 Junior et al, 26 Vassilopoulos et al, 28 Cai et al, 30 Al-Assadi et al, 32 Kumar et al, 33 Ma et al, 34 Zhaohua, 35 Xu et al, 36 Barsoum et al, 37 Zhang and Lin, 38 Mohanty et al, 40 Uygur et al, 41 Xiang et al, 42 Vadood et al, 43 Mishra et al, 44 Mohanty, 45 Liu et al, 46 Martinez and Ponce, 47 Barbosa et al, 48 Lotfi and Beiss, 49 Razzaq et al, 50 Srinivasan et al, 52 Al-Assaf and El Kadi, 53 Park and Kang, 54 Vassilopoulos et al, 55 Majidian and Saidi,…”

Section: Datasets Publicationsmentioning

confidence: 99%

“…Activation function Sigmoid Figueira Pujol and Andrade Pinto, 15 Susmikanti, 17 Rohman et al, 18 Han, 20 Aymerich and Serra, 21 Sohn and Bae, 24 Genel, 25 Junior et al, 26 Vassilopoulos et al, 28 Liao et al, 31 Al-Assadi et al, 32 Kumar et al, 33 Yang et al, 39 Uygur et al, 41 Martinez and Ponce, 47 Barbosa et al, 48 Srinivasan et al, 52 Park and Kang, 54 Vassilopoulos et al, 55 Majidian and Saidi, 56 Xiao et al, 59 Al-Assadi et al, 61 Jin et al, 67 Tapkin, 68 Moghaddam et al, 69 Durodola et al, 70 Durodola et al, 71 Yang et al 73 and Ahmad et al 82…”

Section: Functional Publicationsmentioning

confidence: 99%

“…Yang et al, 1 Al Assaf and El Kadi, 12 Bezazi et al, 13 Figueira Pujol and Andrade Pinto, 15 Susmikanti, 17 Rohman et al, 18 Sohn and Bae, 24 Genel, 25 Junior et al, 26 Vassilopoulos et al, 28 Mathur et al, 29 Cai et al, 30 Al-Assadi et al, 32 Ma et al, 34 Zhaohua, 35 Mohanty et al, 40 Uygur et al, 41 Xiang et al, 42 Mishra et al, 44 Mohanty, 45 Martinez and Ponce, 47 Barbosa et al, 48 Lotfi and Beiss, 49 Razzaq et al, 50 Srinivasan et al, 52 Al-Assaf and El Kadi, 53 Park and Kang, 54 Vassilopoulos et al, 55 Mohanty et al, 58 Mohanty et al, 60 Al-Assadi et al, 61 Karakas, 63 Peng et al, 64 Abdalla and Hawileh, 65 Karakas and Tomasella, 66 Jin et al, 67 Tapkin, 68 Durodola et al, 70 Yan et al, 72 Yang et al, 73 El Kadi and Al-Assaf, 74 Abdalla and Hawileh, 75 Gao et al, 76 El Kadi and Al-Assaf, 77 Li et al, …”

Section: Applied Researchesmentioning

confidence: 99%

See 2 more Smart Citations

Soft computing methods for fatigue life estimation: A review of the current state and future trends

Kalaycı

Karagöz

Karakaş

2020

Fatigue Fract Eng Mat Struct

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Fatigue causes cracking or breakage in a material due to repeated loads; it causes the material to become unusable. Therefore, knowing the fatigue life of materials is crucial for the implementation of designs, economy and human life. Soft computing methodologies, a subset of artificial intelligence emerging to simulate human intelligence, deal with approximate models and seek solutions to complex real-life problems relying on both computational power of machines and the high accuracy of the algorithms. In this study, soft computing methods adapted for estimating/predicting the fatigue life of engineering structures and materials are investigated. For this purpose, 95 articles published between 1995 and 2020 have been examined in detail. With this review, it is aimed to reveal the efficiency of soft computing methods and contribute to their development. Recommendations have been made to draw attention to these methodologies which are expected to be used in many areas in the future.

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Section: Datasets Publicationsmentioning

confidence: 99%

Section: Functional Publicationsmentioning

confidence: 99%

Section: Applied Researchesmentioning

confidence: 99%

See 1 more Smart Citation

Soft computing methods for fatigue life estimation: A review of the current state and future trends

Kalaycı

Karagöz

Karakaş

2020

Fatigue Fract Eng Mat Struct

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“…Several failure modes of structural elements have also been investigated with the help of AI approaches. For instance, fatigue of steel components was studied using neural network algorithm [43]. In the works of Tan et al [44] and Padil et al [45], damage in steel beams has been detected, located and quantified using a non-probabilistic artificial neural network model.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression

Pham

et al. 2019

Materials

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This study aims to investigate the prediction of critical buckling load of steel columns using two hybrid Artificial Intelligence (AI) models such as Adaptive Neuro-Fuzzy Inference System optimized by Genetic Algorithm (ANFIS-GA) and Adaptive Neuro-Fuzzy Inference System optimized by Particle Swarm Optimization (ANFIS-PSO). For this purpose, a total number of 57 experimental buckling tests of novel high strength steel Y-section columns were collected from the available literature to generate the dataset for training and validating the two proposed AI models. Quality assessment criteria such as coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to validate and evaluate the performance of the prediction models. Results showed that both ANFIS-GA and ANFIS-PSO had a strong ability in predicting the buckling load of steel columns, but ANFIS-PSO (R2 = 0.929, RMSE = 60.522 and MAE = 44.044) was slightly better than ANFIS-GA (R2 = 0.916, RMSE = 65.371 and MAE = 48.588). The two models were also robust even with the presence of input variability, as investigated via Monte Carlo simulations. This study showed that the hybrid AI techniques could help constructing an efficient numerical tool for buckling analysis.

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“…Kang et al [9] showed how ANN could be used to reduce computational time for fatigue damage calculation under multiaxial random loading for a component. Recently, Martinez and Ponce [10] showed that ANN could be used to predict the effect of temperature on fatigue damage during different sequences of loading of a component. In the realm of random loading fatigue, Kim et al [1] showed the possibility of using ANN to determine the stress range probability density function for two peak spectral load data type indicating that better performance is obtained compared to those developed by Wirsching-Light [11], Zhao-Baker [12], Benasciutti-Tovo [13], Tovo [14] and Dirlik [15].…”

Section: Introductionmentioning

confidence: 99%

Experimental validation of an ANN model for random loading fatigue analysis

Ramachandra

Durodola

Fellows

et al. 2019

International Journal of Fatigue

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The use of artificial intelligence especially based on artificial neural networks (ANN) is now prevalent in many fields of data analysis and interpretation. There have been a number of papers published in the literature on the use of ANN for fatigue characterisation. Most of these have however been developed for rather focussed application with limited capability for fatigue life prediction for a broad scope of material and loading conditions. The authors recently presented a uniquely generalised ANN model that is capable of making fatigue life prediction for a broad range of material fatigue properties and loading spectral forms. The model was developed using simulated data albeit subject to conceivable constraints between possible materials properties and load forms. This paper presents a validation of the ANN model using a Society of Automotive Engineers (SAE) random fatigue loading experimental test data. The capabilities and potentials of the model are demonstrated by comparison with the SAE random load fatigue test results and with results obtained from other predictive methods. The performance of the ANN is highly encouraging as a general tool for random loading fatigue analysis.

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Fatigue damage effect approach by artificial neural network

Cited by 50 publications

References 25 publications

Soft computing methods for fatigue life estimation: A review of the current state and future trends

Soft computing methods for fatigue life estimation: A review of the current state and future trends

Hybrid Artificial Intelligence Approaches for Predicting Buckling Damage of Steel Columns Under Axial Compression

Experimental validation of an ANN model for random loading fatigue analysis

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