Efficient structural reliability analysis based on adaptive Bayesian support vector regression

Wang, Jinsheng; Li, Chenfeng; Xu, Guoji; Li, Yongle; Kareem, Ahsan

doi:10.1016/j.cma.2021.114172

Cited by 49 publications

(6 citation statements)

References 90 publications

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“…where 2 ( )  x is the predictive variance of the Bayesian support vector regression, which can be calculated from Equation (10), and…”

Section: The Proposed New Error-pursuing Active Learning Functionmentioning

confidence: 99%

“…where ∑ 2 (x) is the predictive variance of the Bayesian support vector regression, which can be calculated from Equation (10), and e LOO (x) is the leave-one-out (LOO) cross-validation error.…”

Section: The Proposed New Error-pursuing Active Learning Functionmentioning

confidence: 99%

“…Recently, uncertainty analysis methods based on Bayesian support vector regression (BSVR) have gained widespread attention [10,11]. BSVR is a special support vector regression (SVR) model that is derived from the Bayesian inference framework.…”

Section: Introductionmentioning

confidence: 99%

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An Error-Pursuing Adaptive Uncertainty Analysis Method Based on Bayesian Support Vector Regression

et al. 2023

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The Bayesian support vector regression (BSVR) metamodel is widely used in various engineering fields to analyze the uncertainty arising from uncertain parameters. However, the accuracy of the BSVR metamodel based on the traditional one-shot sampling method fails to meet the requirements of the uncertainty analysis of complex systems. To this end, an error-pursing adaptive uncertainty analysis method based on the BSVR metamodel is presented by combining a new adaptive sampling scheme. This new sampling scheme was improved by a new error-pursuing active learning function that is named, herein, adjusted mean square error (AMSE), which guides the adaptive sampling of the BSVR metamodel’s design of experiments (DoE). During the sampling process, AMSE combines mean square error and leave-one-out cross-validation error to estimate the prediction error of the metamodel in the entire design space. Stepwise refinement of the metamodel was achieved by placing the sampled regions at locations with large prediction errors. Six benchmark analytical functions featuring different dimensions were used to validate the proposed method. The effectiveness of the method was then further illustrated by a more realistic application of an overhung rotor system.

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“…where 2 ( )  x is the predictive variance of the Bayesian support vector regression, which can be calculated from Equation (10), and…”

Section: The Proposed New Error-pursuing Active Learning Functionmentioning

confidence: 99%

Section: The Proposed New Error-pursuing Active Learning Functionmentioning

confidence: 99%

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An Error-Pursuing Adaptive Uncertainty Analysis Method Based on Bayesian Support Vector Regression

et al. 2023

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“…Various methods have been developed over the years to estimate the probability of failure, which can generally be classified into four categories, i.e. approximate analytical methods (Lee et al ., 2012; Lopez et al ., 2015), numerical simulation methods (Naess et al ., 2012; Papaioannou et al ., 2016; Alvarez et al ., 2018), moment methods (Zhao and Ono, 2001; He et al ., 2019; Xu and Zhu, 2019; Miao et al ., 2020) and surrogate-based methods (Zhou et al ., 2019; Roy et al ., 2019; Wang et al ., 2021b). The first-order reliability method (FORM) (Shinozuka, 1983; Huang et al ., 2018) and second-order reliability method (SORM) (Der Kiureghian et al ., 1987; Mansour and Olsson, 2014) are two representative approaches in the category of approximate analytical methods, and they approximate the performance function using Taylor series expansion at the most probable failure point (MPP).…”

Section: Introductionmentioning

confidence: 99%

An adaptive Kriging method based on K-means clustering and sampling in n-ball for structural reliability analysis

Wang

Cao

et al. 2023

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PurposeAssessing the failure probability of engineering structures is still a challenging task in the presence of various uncertainties due to the involvement of expensive-to-evaluate computational models. The traditional simulation-based approaches require tremendous computational effort, especially when the failure probability is small. Thus, the use of more efficient surrogate modeling techniques to emulate the true performance function has gained increasingly more attention and application in recent years. In this paper, an active learning method based on a Kriging model is proposed to estimate the failure probability with high efficiency and accuracy.Design/methodology/approachTo effectively identify informative samples for the enrichment of the design of experiments, a set of new learning functions is proposed. These learning functions are successfully incorporated into a sampling scheme, where the candidate samples for the enrichment are uniformly distributed in the n-dimensional hypersphere with an iteratively updated radius. To further improve the computational efficiency, a parallelization strategy that enables the proposed algorithm to select multiple sample points in each iteration is presented by introducing the K-means clustering algorithm. Hence, the proposed method is referred to as the adaptive Kriging method based on K-means clustering and sampling in n-Ball (AK-KBn).FindingsThe performance of AK-KBn is evaluated through several numerical examples. According to the generated results, all the proposed learning functions are capable of guiding the search toward sample points close to the LSS in the critical region and result in a converged Kriging model that perfectly matches the true one in the regions of interest. The AK-KBn method is demonstrated to be well suited for structural reliability analysis and a very good performance is observed in the investigated examples.Originality/valueIn this study, the statistical information of Kriging prediction, the relative contribution of the sample points to the failure probability and the distances between the candidate samples and the existing ones are all integrated into the proposed learning functions, which enables effective selection of informative samples for updating the Kriging model. Moreover, the number of required iterations is reduced by introducing the parallel computing strategy, which can dramatically alleviate the computation cost when time demanding numerical models are involved in the analysis.

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“…In this regard, a method that can accurately predict the wave forces on the bridge decks promptly is essential for the stakeholders to make critical decisions prior to the landfall of hurricanes [7]. Moreover, an effective prediction method can also facilitate the safety assessment of the bridge under a probability-based framework, e.g., structural reliability analysis [8], and enable the efficient structural analysis under the action of other extreme loads such as seismic load [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Surrogate Model for the Prediction of Solitary Wave Forces on the Coastal Bridge Decks

Wang

Xue

2021

Infrastructures

Self Cite

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To facilitate the establishment of the probabilistic model for quantifying the vulnerability of coastal bridges to natural hazards and support the associated risk assessment and mitigation activities, it is imperative to develop an accurate and efficient method for wave forces prediction. With the fast development of computer science, surrogate modeling techniques have been commonly used as an effective alternative to computational fluid dynamics for the establishment of a predictive model in coastal engineering. In this paper, a hybrid surrogate model is proposed for the efficient and accurate prediction of the solitary wave forces acting on coastal bridge decks. The underlying idea of the proposed method is to enhance the prediction capability of the constructed model by introducing an additional surrogate to correct the errors made by the main predictor. Specifically, the regression-type polynomial chaos expansion (PCE) is employed as the main predictor to capture the global feature of the computational model, whereas the interpolation-type Kriging is adopted to learn the local variations of the prediction error from the PCE. An engineering case is employed to validate the effectiveness of the hybrid model, and it is observed that the prediction performance (in terms of residual mean square error and correlation coefficient) of the hybrid model is superior to the optimal PCE and artificial neural network (ANN) for both horizontal and vertical wave forces, albeit the maximum PCE degrees used in the hybrid model are lower than the optimal degrees identified in the pure PCE model. Moreover, the proposed hybrid model also enables the extraction of explicit predictive equations for the parameters of interest. It is expected that the hybrid model could be extended to more complex wave conditions and structural shapes to facilitate the life-cycle structural design and analysis of coastal bridges.

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Efficient structural reliability analysis based on adaptive Bayesian support vector regression

Cited by 49 publications

References 90 publications

An Error-Pursuing Adaptive Uncertainty Analysis Method Based on Bayesian Support Vector Regression

An Error-Pursuing Adaptive Uncertainty Analysis Method Based on Bayesian Support Vector Regression

An adaptive Kriging method based on K-means clustering and sampling in n-ball for structural reliability analysis

A Hybrid Surrogate Model for the Prediction of Solitary Wave Forces on the Coastal Bridge Decks

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