System reliability analysis of slopes using least squares support vector machines with particle swarm optimization

Kang, Fei; Li, Jing-Shuang; Li, Junjie

doi:10.1016/j.neucom.2015.11.122

Cited by 62 publications

(23 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fifth paper "System reliability analysis of slopes using least squares support vector machines with particle swarm optimization" [5] by Kang et al present an intelligent response surface method for evaluating system failure probability of soil slopes based on least squares support vector machines (LSSVM) and particle swarm optimization. A novel machine learning technique LSSVM is adopted to establish the response surface to approximate the limit state function based on the samples generated by computer experiments.…”

Section: Papers Of the Special Issuementioning

confidence: 99%

Editorial preface special issue: Advances in computational intelligence with Internet of Things

2016

View full text Add to dashboard Cite

Section: Papers Of the Special Issuementioning

confidence: 99%

Editorial preface special issue: Advances in computational intelligence with Internet of Things

2016

View full text Add to dashboard Cite

“…The surrogate‐assisted methods construct a surrogate model from a set of observed points to approximate the performance function, and then perform reliability analysis based on this cheap meta‐model. For decades, several types of meta‐models are available for reliability analysis including polynomial chaos expansion (PCE), Kriging (Gaussian Process, GP), support vector machine, and so on.…”

Section: Introductionmentioning

confidence: 99%

Active learning polynomial chaos expansion for reliability analysis by maximizing expected indicator function prediction error

Cheng

Lü

2020

Numerical Meth Engineering

View full text Add to dashboard Cite

Assessing the failure probability of complex aeronautical structure is a difficult task in presence of uncertainties. In this paper, active learning polynomial chaos expansion (PCE) is developed for reliability analysis. The proposed method firstly assigns a Gaussian Process (GP) prior to the model response, and the covariance function of this GP is defined by the inner product of PCE basis function. Then, we show that a PCE model can be derived by the posterior mean of the GP, and the posterior variance is obtained to measure the local prediction error as Kriging model. Also, the expectation of the prediction variance is derived to measure the overall accuracy of the obtained PCE model. Then, a learning function, named expected indicator function prediction error (EIFPE), is proposed to update the design of experiment of PCE model for reliability analysis.This learning function is developed under the framework of the variance-bias decomposition. It selects new points sequentially by maximizing the EIFPE that considers both the variance and bias information, and it provides a dynamic balance between global exploration and local exploitation. Finally, several test functions and engineering applications are investigated, and the results are compared with the widely used Kriging model combined with U and expected feasibility function learning function. Results show that the proposed method is efficient and accurate for complex engineering applications.

show abstract

“…Over the last decade, the application of soft computing predictive tools has increased, due to their capability of establishing nonlinear equations between a set of input-output data. In this field, various types of artificial neural network (ANN) [10] and support vector machines (SVM) [11] have been successfully employed for simulating geotechnical problems. The notable advantage of ANNs is that they can perform by any defined number of neurons, as in their hidden layer [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Predicting Slope Stability Failure through Machine Learning Paradigms

Bui

Moayedi

Gör

et al. 2019

IJGI

View full text Add to dashboard Cite

In this study, we employed various machine learning-based techniques in predicting factor of safety against slope failures. Different regression methods namely, multi-layer perceptron (MLP), Gaussian process regression (GPR), multiple linear regression (MLR), simple linear regression (SLR), support vector regression (SVR) were used. Traditional methods of slope analysis (e.g., first established in the first half of the twentieth century) used widely as engineering design tools. Offering more progressive design tools, such as machine learning-based predictive algorithms, they draw the attention of many researchers. The main objective of the current study is to evaluate and optimize various machine learning-based and multilinear regression models predicting the safety factor. To prepare training and testing datasets for the predictive models, 630 finite limit equilibrium analysis modelling (i.e., a database including 504 training datasets and 126 testing datasets) were employed on a single-layered cohesive soil layer. The estimated results for the presented database from GPR, MLR, MLP, SLR, and SVR were assessed by various methods. Firstly, the efficiency of applied models was calculated employing various statistical indices. As a result, obtained total scores 20, 35, 50, 10, and 35, respectively for GPR, MLR, MLP, SLR, and SVR, revealed that the MLP outperformed other machine learning-based models. In addition, SVR and MLR presented an almost equal accuracy in estimation, for both training and testing phases. Note that, an acceptable degree of efficiency was obtained for GPR and SLR models. However, GPR showed more precision. Following this, the equation of applied MLP and MLR models (i.e., in their optimal condition) was derived, due to the reliability of their results, to be used in similar slope stability problems.

show abstract

System reliability analysis of slopes using least squares support vector machines with particle swarm optimization

Cited by 62 publications

References 56 publications

Editorial preface special issue: Advances in computational intelligence with Internet of Things

Editorial preface special issue: Advances in computational intelligence with Internet of Things

Active learning polynomial chaos expansion for reliability analysis by maximizing expected indicator function prediction error

Predicting Slope Stability Failure through Machine Learning Paradigms

Contact Info

Product

Resources

About