Enhancing PEHD pipes reliability prediction: Integrating ANN and FEM for tensile strength analysis

Ihssan, Srii; Shaik, Nagoor Basha; Belouaggadia, Naoual; Jammoukh, Mustapha; Nasserddine, Alanssari

doi:10.1016/j.apsadv.2024.100630

Applied Surface Science Advances

2024

DOI: 10.1016/j.apsadv.2024.100630

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Enhancing PEHD pipes reliability prediction: Integrating ANN and FEM for tensile strength analysis

Srii Ihssan,

Nagoor Basha Shaik,

Naoual Belouaggadia

et al.

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Cited by 2 publications

References 34 publications

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Rheological modeling of MWCNT-Al₂O₃ hybrid nanofluid using cascade forward neural network method

Kholoud,

Mahmoudi Soumar,

Torabi

et al. 2025

Diamond and Related Materials

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Rheological modeling of MWCNT-Al₂O₃ hybrid nanofluid using cascade forward neural network method

Kholoud,

Mahmoudi Soumar,

Torabi

et al. 2025

Diamond and Related Materials

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Forecasting Influenza Trends Using Decomposition Technique and LightGBM Optimized by Grey Wolf Optimizer Algorithm

Duan,

Li,

Wang

et al. 2024

Mathematics

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Influenza is an acute respiratory infectious disease marked by its high contagiousness and rapid spread, caused by influenza viruses. Accurate influenza prediction is a critical issue in public health and serves as an essential tool for epidemiological studies. This paper seeks to improve the prediction accuracy of influenza-like illness (ILI) proportions by proposing a novel predictive model that integrates a data decomposition technique with the Grey Wolf Optimizer (GWO) algorithm, aiming to overcome the limitations of current prediction methods. Firstly, the most suitable indicators were selected using Spearman correlation coefficient. Secondly, a GWO-LightGBM model was established to obtain the residuals between the predicted and actual values. The residual sequence from the GWO-LightGBM model was then decomposed and corrected using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which led to the development of the GWO-LightGBM-CEEMDAN model. The incorporation of the Baidu Index was shown to enhance the precision of the proposed model’s predictions. The proposed model outperforms comparison models in terms of evaluation metrics such as RMSE and MAPE. Additionally, our study found that the revised Baidu Index indicators show a notable association with ILI trends.

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Enhancing PEHD pipes reliability prediction: Integrating ANN and FEM for tensile strength analysis

Cited by 2 publications

References 34 publications

Rheological modeling of MWCNT-Al₂O₃ hybrid nanofluid using cascade forward neural network method

Rheological modeling of MWCNT-Al₂O₃ hybrid nanofluid using cascade forward neural network method

Forecasting Influenza Trends Using Decomposition Technique and LightGBM Optimized by Grey Wolf Optimizer Algorithm

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