Residual strength prediction of corroded pipelines using multilayer perceptron and modified feedforward neural network

Chen, Zhanfeng; Li, Xuyao; Wang, Wen; Li, Yan; Shi, Lei; Li, Yuxing

doi:10.1016/j.ress.2022.108980

Cited by 32 publications

(4 citation statements)

References 27 publications

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“…Initially, simple feed-forward networks were proposed with information flowing just in one way, from input to output. These networks' capacity to analyze complicated data and reflect links between inputs and outputs was restricted 33 . Recurrent neural networks were introduced in the 1980s and 1990s, where information may flow in loops, and the network can keep a hidden state, allowing it to handle input sequences.…”

Section: Introductionmentioning

confidence: 99%

A novel neural network-based framework to estimate oil and gas pipelines life with missing input parameters

Shaik,

Jongkittinarukorn,

Benjapolakul

et al. 2024

Sci Rep

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Dry gas pipelines can encounter various operational, technical, and environmental issues, such as corrosion, leaks, spills, restrictions, and cyber threats. To address these difficulties, proactive maintenance and management and a new technological strategy are needed to increase safety, reliability, and efficiency. A novel neural network model for forecasting the life of a dry gas pipeline system and detecting the metal loss dimension class that is exposed to a harsh environment is presented in this study to handle the missing data. The proposed strategy blends the strength of deep learning techniques with industry-specific expertise. The main advantage of this study is to predict the pipeline life with a significant advantage of predicting the dimension classification of metal loss simultaneously employing a Bayesian regularization-based neural network framework when there are missing inputs in the datasets. The proposed intelligent model, trained on four pipeline datasets of a dry gas pipeline system, can predict the health condition of pipelines with high accuracy, even if there are missing parameters in the dataset. The proposed model using neural network technology generated satisfactory results in terms of numerical performance, with MSE and R2 values closer to 0 and 1, respectively. A few cases with missing input data are carried out, and the missing data is forecasted for each case. Then, a model is developed to predict the life condition of pipelines with the predicted missing input variables. The findings reveal that the model has the potential for real-world applications in the oil and gas sector for estimating the health condition of pipelines, even if there are missing input parameters. Additionally, multi-model comparative analysis and sensitivity analysis are incorporated, offering an extensive comprehension of multi-model prediction abilities and beneficial insights into the impact of various input variables on model outputs, thereby improving the interpretability and reliability of our results. The proposed framework could help business plans by lowering the chance of severe accidents and environmental harm with better safety and reliability.

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Section: Introductionmentioning

confidence: 99%

A novel neural network-based framework to estimate oil and gas pipelines life with missing input parameters

Shaik,

Jongkittinarukorn,

Benjapolakul

et al. 2024

Sci Rep

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“…Artificial neural network (ANN) is a powerful tool for the design of new materials and prediction of complex phenomena. The type of ANN of particular interest is the multilayer perceptron (MLP) which has been used extensively for the prediction of complex phenomena across many fields (Bikku et al (2020); Sanzana et al (2023); Choi et al (2023); Park et al (2021); Chen et al (2023)). The application of an artificial neural network model in predicting the optical properties of plasmonic metasurfaces of spherical core shells is developed by Vahidzadeh et al (2021).…”

Section: Introductionmentioning

confidence: 99%

Prediction of optical properties of uniaxial hyperbolic nanospheres via artificial neural network

Edward,

Johnson

2024

Preprint

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In this study, absorption and scattering multilayer perceptron models are developed and validated for the prediction of the optical spectra of uniaxial hyperbolic nanospheres. The finite difference time domain method is used to generate the dataset of the absorption and scattering optical spectra. The models' optimal performance is achieved for 5 hidden layers and 80 neurons for the absorption and the same hidden layers with 120 neurons for the scattering. The predictions by the model on the test dataset give a low aggregate mean squared error of 0.000145 for the absorption and 0.00071 for the scattering. We also performed a robustness test by using parameters outside the initial parameters used for the training and the predictions are in good agreement with the actual datasets for both absorption and scattering. This research shows the application of artificial neural network to predict the optical properties of hyperbolic materials and lay a groundwork for developing more complicated neural network models in predicting complex phenomena in hyperbolic metamaterials which are faster and computationally less expensive than using conventional simulation methods.

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“…Chen et al [ 43 ] utilized an artificial neural network (ANN) to predict the residual strength of corroded natural gas pipelines. Overcoming challenges like limited training data and overfitting, innovative techniques such as ReLU activation and dropout methods were employed.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach for Pitting Corrosion Detection in Gas Pipelines

Malashin,

Tynchenko,

Nelyub

et al. 2024

Sensors

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The paper introduces a computer vision methodology for detecting pitting corrosion in gas pipelines. To achieve this, a dataset comprising 576,000 images of pipelines with and without pitting corrosion was curated. A custom-designed and optimized convolutional neural network (CNN) was employed for binary classification, distinguishing between corroded and non-corroded images. This CNN architecture, despite having relatively few parameters compared to existing CNN classifiers, achieved a notably high classification accuracy of 98.44%. The proposed CNN outperformed many contemporary classifiers in its efficacy. By leveraging deep learning, this approach effectively eliminates the need for manual inspection of pipelines for pitting corrosion, thus streamlining what was previously a time-consuming and cost-ineffective process.

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Residual strength prediction of corroded pipelines using multilayer perceptron and modified feedforward neural network

Cited by 32 publications

References 27 publications

A novel neural network-based framework to estimate oil and gas pipelines life with missing input parameters

A novel neural network-based framework to estimate oil and gas pipelines life with missing input parameters

Prediction of optical properties of uniaxial hyperbolic nanospheres via artificial neural network

Deep Learning Approach for Pitting Corrosion Detection in Gas Pipelines

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