Research on Vibration Fatigue Damage Locations of Offshore Oil and Gas Pipelines Based on the GA-Improved BP Neural Network

Xie, Yaoguo; Gao, Chengang; Wang, Puzhe; Zhou, Lei; Zhang, Chuanjie; Qu, Xianqiang

doi:10.1155/2023/2530651

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…This function uses the gradient method to iteratively update the weight threshold of each layer connection until the minimum error is obtained. The learning function of the neural network uses the gradient descent momentum function learned [17] or [28][29][30]. The target mean square error is set to 0.001, and the maximal frequency of training is 5000.…”

Section: Pca-bp Neural Network Modelingmentioning

confidence: 99%

Prediction Model of Water Abundance of Weakly Cemented Sandstone Aquifer Based on Principal Component Analysis–Back Propagation Neural Network of Grey Correlation Analysis Decision Making

Kuo,

Li,

Zhang

et al. 2024

Water

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At present, in the vast majority of coal mine production processes in China, the degree of hydrogeological exploration often lags behind geological exploration. The main difficulty in evaluating the water richness of coal seam top and bottom water-bearing beds is that the existing evaluation methods often rely on less hydrogeological investigation data. How to utilize the abundant geological exploration data in the mining area to appraise the water-rich distribution of sandstone aquifers is a feasible and challenging methodology. At present, some experts and scholars have tried to use multivariate factor analysis to solve the problem of water-richness evaluation, and they have achieved certain results, but there are some shortcomings: (1) The prediction results are mostly qualitative estimations of the water-richness grade, and there is a lack of quantitative analysis of the units-inflow; and (2) at present, the more advanced prediction methods, such as the back propagation (BP) neural network model, have the disadvantages of low accuracy, requiring many iterations, and slow convergence speed. Therefore, with geological exploration data of the 1503E working face of the Yili No.1 coal mine as the basis., this paper uses grey correlation analysis to screen out the factor indexes suitable for the evaluation of the water richness of a weakly cemented sandstone aquifer, and it combines principal component analysis (PCA) with a BP neural network. Based on the selected factor indexes, a prediction model of the water richness of a weakly cemented sandstone aquifer is established. The results show that compared with the existing methods, the prediction accuracy is higher and has a certain application value.

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Section: Pca-bp Neural Network Modelingmentioning

confidence: 99%

Prediction Model of Water Abundance of Weakly Cemented Sandstone Aquifer Based on Principal Component Analysis–Back Propagation Neural Network of Grey Correlation Analysis Decision Making

Kuo,

Li,

Zhang

et al. 2024

Water

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“…Thus, the network structure of a single hidden layer is adopted. As for the number of nodes in the hidden layer, too many nodes may lead to a massive amount of computation, while too few nodes may reduce the model's accuracy (Tang et al, 2023;Xie et al, 2023). Thus, the number of hidden layer nodes is usually determined according to the empirical formula, as shown in Eq.…”

Section: Structure Design Of Bp Neural Networkmentioning

confidence: 99%

Application research on the prediction of tar yield of deep coal seam mining areas based on PSO-BPNN machine learning algorithm

Qiao

Wang

et al. 2023

Front. Earth Sci.

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There are abundant deep coal resources in northern Shaanxi, but the fragile natural environment in this area hinders the large-scale exploitation of oil-rich coal. In-situ thermal conversion of deep coal to oil and gas will become an environmentally friendly technology for oil-rich coal mining. Accurate prediction of oil-rich coal tar yield in various regions is a prerequisite. Based on a particle swarm optimization algorithm and two machine learning algorithms, BP neural network and random forest, a prediction model of tar yield from oil-rich coal is constructed in this paper. Combined with the particle swarm optimization method, the problem of slow convergence speed and possibly falling into local minimum value of BP neural network is solved and optimized. The results showed that the PSO-BP had a convergence speed about five times faster than that of the BP neural network. Furthermore, the predicted value of the PSO-BP was consistent with the measured value, and the average relative error was 4.56% lower than that of the random forest model. The advantages of fast convergence and high accuracy of the prediction model are obviously apparent. Accurate prediction of tar yield would facilitate the research process of in-situ fluidized mining of deep coal seams.

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“…Generally, damage detection methods based on ANN do not achieve all Rytter's three levels (Hekmati Athar et al [32]: level 1, Weinstein et al [33]: levels 1 and 2, Xie et al [34]: levels 1 and 2, Bisheh et al [35]: level 1), or the authors propose multistage approaches to complete the three tasks (Malekjafarian et al [36]: 2-stage method, Nick et al [37]: 2stage method). Detection typically involves identifying whether damage is present, followed by separate steps to locate the damage and quantify its severity.…”

Section: Introductionmentioning

confidence: 99%

Detection, Localization, and Quantification of Damage in Structures via Artificial Neural Networks

Kauctz Monteiro,

Miguel,

Zeni

et al. 2023

Shock and Vibration

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This paper presents a structural health monitoring method based on artificial neural networks (ANNs) capable of detecting, locating, and quantifying damage in a single stage. The proposed framework employs a supervised neural network model that uses input factors calculated by modal parameters (natural frequencies or mode shapes), and output factors that represent the damage situation of elements or regions in a structural system. Unlike many papers in the literature that test damage detection methods only in numerical examples or simple experimental tests, this work also assesses the presented method in a real structure showing that it has potential for applications in real practical situations. Three different cases are evaluated through the methodology: numerical simulations, an experimental lab structure, and a real bridge. Initially, a cantilever beam and a 10-bar truss were numerically analyzed under ambient vibrations with different damage scenarios and noise levels. Afterward, the method is assessed in an experimental beam structure and in the Z24 bridge benchmark. The numerical simulations showed that the methodology is promising for identifying, locating, and quantifying single and multiple damages in a single stage, even with noise in the acceleration signals and changes in the first vibration mode of 0.015%. In addition, the Z24 bridge study confirmed that the damage detection method can localize damage in real civil structures considering only natural frequencies in the input factors, despite a mean difference of 4.08% between the frequencies in the healthy and damaged conditions.

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Research on Vibration Fatigue Damage Locations of Offshore Oil and Gas Pipelines Based on the GA-Improved BP Neural Network

Cited by 5 publications

References 20 publications

Prediction Model of Water Abundance of Weakly Cemented Sandstone Aquifer Based on Principal Component Analysis–Back Propagation Neural Network of Grey Correlation Analysis Decision Making

Prediction Model of Water Abundance of Weakly Cemented Sandstone Aquifer Based on Principal Component Analysis–Back Propagation Neural Network of Grey Correlation Analysis Decision Making

Application research on the prediction of tar yield of deep coal seam mining areas based on PSO-BPNN machine learning algorithm

Detection, Localization, and Quantification of Damage in Structures via Artificial Neural Networks

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