Efficient prediction of wind and wave induced long-term extreme load effects of floating suspension bridges using artificial neural networks and support vector machines

Yang, Xu; Fenerci, Aksel; Moan, Torgeir

doi:10.1016/j.oceaneng.2020.107888

Cited by 19 publications

(6 citation statements)

References 35 publications

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“…Based on the aforementioned analysis, it is evident that the computational expense of FEM is substantial. Therefore, the SVM model, which exhibits exceptional nonlinear fitting capabilities, is an optimal choice for enhancing computational efficiency [18]. To generate the internal corrosion defect model in batches, the methodology outlined in Chapter 1.1 is implemented using a Python script.…”

Section: Database Construction Of Machine Learning Prediction Modelmentioning

confidence: 99%

Application of Improved Support Vector Machine in Predicting Failure Pressure of Oil and Gas Pipelines with Internal Corrosion Defects

Yuan,

Zhang,

Ren

2024

AJST

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In this study, the failure pressure of submarine oil and gas pipelines was predicted by using five commonly used machine learning models and derivative models. Firstly, an efficient local time-intensive finite element algorithm is constructed and used to generate a machine learning database. Secondly, in order to improve the accuracy of machine learning algorithm, the frontier optimization algorithm is improved to form a new predictive regression model IAEO-SVM model. To compare the accuracy of commonly used machine learning models and IAEO-SVM models, a comprehensive evaluation standard consisting of k-fold cross-validation and three statistical indicators was used. Finally, the influence of seawater depth and geometric factors of corrosion defects on the blasting pressure of submarine oil and gas pipelines was comprehensively analyzed through the high-dimensional surface built by the IAEO-SVM model. The IAEO-SVM model shows superior stability and accuracy compared to the comparison model, as demonstrated by its MSE’ of 0.0482, R2’ of 0.9982, MAE’ of 0.1295, and SD of 0.2198. The high-dimensional surface obtained through inversion shows a linear relationship between seawater depth and failure pressure. Meanwhile, the width of corrosion defects has a significant impact on failure pressure, accounting for up to 23% and thus cannot be overlooked.

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Section: Database Construction Of Machine Learning Prediction Modelmentioning

confidence: 99%

Application of Improved Support Vector Machine in Predicting Failure Pressure of Oil and Gas Pipelines with Internal Corrosion Defects

Yuan,

Zhang,

Ren

2024

AJST

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“…A. SVM SVM has a strong classification ability [34]. The principle is to construct the optimal hyperplane in space and classify the sample data.…”

Section: Tisoa-svmmentioning

confidence: 99%

A Power Transformer Fault Diagnosis Method-Based Hybrid Improved Seagull Optimization Algorithm and Support Vector Machine

Sun

Zhang

et al. 2022

IEEE Access

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The method of Support Vector Machine (SVM) based on Dissolved Gas Analysis (DGA) has been studied in the field of power transformer fault diagnosis. However, there are still some shortcomings, such as the fuzzy boundaries of DGA data, and SVM parameters are difficult to determine. Therefore, this paper proposes a power transformer fault diagnosis method based on Kernel Principal Component Analysis (KPCA) and a hybrid improved Seagull Optimization Algorithm to optimize the SVM (TISOA-SVM). Firstly, KPCA is used to extract features from DGA feature quantities. In addition, TISOA is further proposed to optimize the SVM parameters to build the optimal diagnosis model based on SVM. For the SOA, three improvement methods are proposed. An improved tent map is used to replace the original population initialization to improve population diversity. In addition, the nonlinear inertia weight and random double helix formula are proposed to improve the optimization accuracy and efficiency of the SOA. Then, benchmarking functions are used to test the optimization performance of TISOA and six algorithms, and the results show that TISOA has the best optimization accuracy and convergence speed. Finally, the fault diagnosis method based on KPCA and TISOA-SVM is obtained, and it is noteworthy that three examples are tested to verify the diagnostic performance of the proposed method. These results show that the proposed method has higher diagnostic accuracy, shorter diagnosis time, stronger significance and validity than other methods. Therefore, a research idea is provided for solving practical engineering problems in the field of fault diagnosis.INDEX TERMS Power transformer, fault diagnosis, kernel principal component analysis, support vector machine, hybrid improved seagull optimization algorithm.

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“…Over the years, many studies have explored the possibility to reduce this computational burden for various marine structures, such as floating offshore wind turbines [4] or floating bridges [18], by focusing on the long-term analysis and by using for instance the first and second order reliability methods, the inverse reliability method, or the environmental contour approach but also surrogate modelling or learning algorithms [17,21,45,30,22]. Apart from that, another way for improving the computational efficiency of the long-term analyses is to accelerate each of the many short-term analyses that have to be conducted.…”

Section: Introductionmentioning

confidence: 99%

Multiple Timescale Spectral Analysis of Floating Structures Subjected to Hydrodynamic Loads

et al. 2023

Self Cite

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The determination of the long-term extreme distribution for a wave-loaded structure requires to compute the second order statistics of the responses and their time derivatives under various short-term sea states. In a spectral context, these statistics are typically obtained in the modal basis by integrating the cross-spectral densities of the corresponding responses over the frequency. In this paper, a semianalytical approximation is developed for computing these integrals, in order to reduce the computational cost of each short-term analysis. To do so, a state-space formulation is considered for the equations of motion and the general framework provided by the multiple timescale spectral analysis is implemented. It hinges on the existence of distinct peaks in the integrands to express the variances and the covariances of the modal state responses as the sum of two components with simple expressions: the resonant and the loading component. The proposed approximation is validated on a minimalistic example first and is then verified on a simplified model inspired by the Bergsøysund Bridge, an actual floating pontoon bridge.

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Efficient prediction of wind and wave induced long-term extreme load effects of floating suspension bridges using artificial neural networks and support vector machines

Cited by 19 publications

References 35 publications

Application of Improved Support Vector Machine in Predicting Failure Pressure of Oil and Gas Pipelines with Internal Corrosion Defects

Application of Improved Support Vector Machine in Predicting Failure Pressure of Oil and Gas Pipelines with Internal Corrosion Defects

A Power Transformer Fault Diagnosis Method-Based Hybrid Improved Seagull Optimization Algorithm and Support Vector Machine

Multiple Timescale Spectral Analysis of Floating Structures Subjected to Hydrodynamic Loads

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