Support vector machine in structural reliability analysis: A review

Roy, Atin; Chakraborty, Subrata

doi:10.1016/j.ress.2023.109126

Cited by 124 publications

(53 citation statements)

References 84 publications

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“…Besides categorizing in linear space, the SVM technique can be easily applied to nonlinear categorization as well by using Kernel techniques to covertly move the inputs into high-dimensional feature spaces. 35…”

Section: Discussionmentioning

confidence: 99%

“…In Figure 5, example of SVM is depicted. 34,35 The issue of distinguishing between two distinct classes of training vectors, {(x1, y1), (x2, y2),Á Á Á, (xp, yp)}, where x2 Rn and y2 { + 1, 21}, can be addressed with a hyperplane defined as wÁx + b = 0. The collection of vectors can be optimally separated by the hyperplane when it is divided with no errors and the distance of the closest vector to the hyperplane is maximized.…”

Section: Support Vector Machine (Svm) Support Vectormentioning

confidence: 99%

“…This is effectively the same as reducing ||w||/2, thereby resulting in a Quadratic Programming (QP) problem as follows: Besides categorizing in linear space, the SVM technique can be easily applied to nonlinear categorization as well by using Kernel techniques to covertly move the inputs into high-dimensional feature spaces. 35 The parameters that can be optimized to get high prediction ability in SVM are:…”

Section: Support Vector Machine (Svm) Support Vectormentioning

confidence: 99%

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Analysis of heat transfer enhancement of passive methods in tubes with machine learning

Özgirgin Yapıcı,

Aylı,

Türkoğlu

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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This study investigates the efficacy of machine learning techniques and correlation methods for predicting heat transfer performance in a dimpled tube under varying flow conditions, including the presence of nanoparticles. A comprehensive numerical analysis involving 120 cases was conducted to obtain Nusselt numbers and friction factors, considering different dimple depths and velocities for both pure water and water-Al2O3 nanofluid at 1%, 2%, and 3% volume concentrations. Utilizing the data acquired from the numerical simulations, a correlation equation, SVM ANN architectures were developed. The predictive capabilities of the statistical approach, ANN, and SVM models for Nusselt number distribution and friction factor were meticulously assessed through mean average percentage error (MAPE) and correlation coefficients ( R2). The research findings reveal that machine learning techniques offer a highly effective approach for accurately predicting heat transfer performance in a dimpled tube, with results closely aligned with Computational Fluid Dynamics (CFD) simulations. Particularly noteworthy is the superior performance of the ANN model, demonstrating the most precise predictions with an error rate of 2.54% and an impressive R2 value of 0.9978 for Nusselt number prediction. In comparison, the regression model achieved an average error rate of 6.14% with an R2 value of 0.8623, and the SVM model yielded an RMSE value of 2.984% with an R2 value of 0.9154 for Nusselt number prediction. These outcomes underscore the ANN model’s ability to effectively capture complex patterns within the data, resulting in highly accurate predictions. In conclusion, this research showcases the promising potential of machine learning techniques in accurately forecasting heat transfer performance in dimpled tubes. The developed ANN model exhibits notable superiority in predicting Nusselt numbers, making it a valuable tool for enhancing thermal system analyses and engineering design optimization.

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Section: Discussionmentioning

confidence: 99%

Section: Support Vector Machine (Svm) Support Vectormentioning

confidence: 99%

Section: Support Vector Machine (Svm) Support Vectormentioning

confidence: 99%

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Analysis of heat transfer enhancement of passive methods in tubes with machine learning

Özgirgin Yapıcı,

Aylı,

Türkoğlu

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…SVR is effective in high-dimensional spaces and is robust to overfitting, which is particularly useful for non-linear data (Roy and Chakraborty, 2023;Liao et al, 2024).…”

Section: Modeling Evaluationmentioning

confidence: 99%

Sources Identification of Water Inrush in Coal Mines Using Technique of Multiple Non-Linear Machine Learning Modelling

Shan¹

2021

Groundwater Management and Resources

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Water inrush is a major threat to the working safety for coal mines in the Northern China coal district. The inrush pattern, threaten level, and also the geochemical characteristics varies according to the different of water sources. Therefore, identifying the water source correctly is an important task to predict and control the water inrush accidents. In this chapter, the algorithms and attempts to identify the water inrush sources, especially in the Northern China coal mine district, are reviewed. The geochemical and machine learning algorithms are two main methods to identify the water inrush sources. Four main steps need to apply, namely data processing, feature selection, model training, and evaluation, in the process of machine learning (ML) modelling. According to a calculation instance, most of the major ions, and some trace elements, such as Ti, Sr, and Zn, were identified to be important in light of geochemical analysis and machine learning modelling. The ML algorithms, such as random forest (RF), support vector machine (SVM), Logistica regression (LR) perform well in the source identification of coal mine water inrush.

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“…To address the above problems, the reliability analysis method based on the surrogate model has emerged and received more and more attention in recent years. 12 The commonly used surrogate models include: Kriging model, [13][14][15] artificial neural networks (ANN), 16,17 response surface method (RSM), 18,19 support vector machine (SVM), 20,21 etc. Since the Kriging model has the advantages in nonlinear function fitting, good continuity, and drivability, Kriging-based reliability analysis has become a research hotspot in the field of reliability.…”

Section: Introductionmentioning

confidence: 99%

An active learning Kriging‐based multipoint sampling strategy for structural reliability analysis

Tian

Zhi

Guan

et al. 2023

Quality & Reliability Eng

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In order to effectively and accurately assess the failure probability of mechanical structures, this paper proposes a multi‐point sampling active learning reliability analysis method called AKMP. First, a GA‐Halton sequence is introduced to make the initial samples well dispersed and homogeneous in the design space. Second, a new learning function FELF is constructed to efficiently update the Kriging model, which takes into account the relationship between the location of the sampling points and the performance fun. Then, a combination of NCC criterion and multipoint sampling strategy is proposed to further improve the convergence efficiency, which can effectively terminate the active learning process. Finally, numerical and engineering cases are tested to verify the application performance of the proposed AKMP. The results show that the method has superior performance in terms of both accuracy and failure probability efficiency, and can reduce the computational resources of the active learning process.

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Support vector machine in structural reliability analysis: A review

Cited by 124 publications

References 84 publications

Analysis of heat transfer enhancement of passive methods in tubes with machine learning

Analysis of heat transfer enhancement of passive methods in tubes with machine learning

Sources Identification of Water Inrush in Coal Mines Using Technique of Multiple Non-Linear Machine Learning Modelling

An active learning Kriging‐based multipoint sampling strategy for structural reliability analysis

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