Response Surface Method of Reliability Analysis and its Application in Slope Stability Analysis

Tan, Xuezhi; Shen, Mengfen; Hou, Xiyong; Li, Dan; Hu, Na

doi:10.1007/s10706-013-9628-4

Cited by 37 publications

(11 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various response surface methods have been developed for the reliability analysis of geotechnical and geological engineering problems. Quadratic polynomials without cross-terms were adopted in the reliability analysis of geotechnical engineering problems based on the RSM ( Xu and Low, 2006 ; Zhang et al, 2011 ; Ji and Low, 2012 ; Tan et al, 2013 ; Li et al, 2015a , Li et al, 2015b ). Lv and Low (2011) performed the probabilistic analysis of underground rock excavation using the RSM and the second-order reliability method (SORM) ( Lv and Low, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Data-driven reduced order model and simplicial homology global optimization for reliability analysis and application

Zhao¹,

Wang²,

Xu³

2022

Heliyon

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Data-driven reduced order model and simplicial homology global optimization for reliability analysis and application

Zhao¹,

Wang²,

Xu³

2022

Heliyon

View full text Add to dashboard Cite

“…After the application, it was proved that the accuracy of this method was better than the (first-order reliability method) FORM method, and the efficiency was higher than the ordinary MCS method. Tan et al [25] proposed a method to replace the original functional function in reliability analysis with an SVM and (radial basis function) RBF model fitted based on two new sample selection methods and combined different examples to illustrate the accuracy and efficiency of the two. Su [26] constructed a Monte Carlo method based on Gaussian regression response surface and verified the effectiveness and efficiency of the method through three slope calculation examples.…”

Section: Introductionmentioning

confidence: 99%

An Improved Monte Carlo Method Based on Neural Network and Fuzziness Analysis: A Case Study of the Nanpo Dump of the Chengmenshan Copper Mine

Gao

2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The landslide of dump is a man-made geological disaster which will bring great harm to the surrounding people and environment, and probabilistic reliability analysis is commonly used to analyze the probability of slope landslide or whether protective measures should be taken. Monte Carlo simulation is the most commonly used method, but there are some problems, such as low efficiency, statistical ambiguity of small samples, and the fuzzy transition interval of the stability criterion. This paper proposes an improved Monte Carlo method that uses an improved bootstrap method to process small samples of geotechnical data, employs ELM (extreme learning machine) based on PSO (particle swarm optimization) to fit the limit equilibrium method function, and constructs the safety factor membership function of the dump site considering the fuzzy transition interval. This method was applied to an example slope of the dump site in Chengmenshan, Jiangxi. Comparing the analysis result with the result of the traditional MCS (Monte Carlo Search) method, it was found that after adding the safety factor membership function, the result was closer to the actual situation of the dump site, and the probability of failure and reliability index values were closer to those of the dangerous state; after the original function was replaced by the PSO-ELM model, the efficiency of the MCS method was greatly improved while the results maintained high consistency with the original results; the MCS method combined with the bootstrap method not only simulated the fuzzy uncertainty of the original sample statistics and distribution type but also expressed the reliability index and probability of failure as a two-sided confidence interval with a certain confidence level. The above conclusion proves the effectiveness and superiority of this method compared with the original MCS method.

show abstract

“…Although the RSM is a well-established method, it has been applied to limited number of geostructures such as rock-fills [9], concrete-faced rock-fill dams [10], slope stability reliability analysis [11][12][13], and concrete gravity dams [14,15].…”

Section: Introductionmentioning

confidence: 99%

Response Surface Method for Material Uncertainty Quantification of Infrastructures

Hariri-Ardebili

Kolbadi²,

Noori

2018

Shock and Vibration

View full text Add to dashboard Cite

Recently, probabilistic simulations became an inseparable part of risk analysis. Managers and stakeholders prefer to make their decision knowing the existing uncertainties in the system. Nonlinear dynamic analysis and design of infrastructures are affected by two main uncertainty sources, i.e., epistemic and aleatory. In the present paper, the epistemic uncertainty is addressed in the context of material randomness. An old ultra-high arch dam is selected as a vehicle for numerical analyses. Four material properties are selected as random variables in the coupled dam-reservoir-foundation system, i.e., concrete elasticity, mass density, compressive (and tensile) strength, and the rock modulus of elasticity. The efficient Box-Behnken experimental design is adopted to minimize the required simulations. A response surface metamodel is developed for the system based on different outputs, i.e., displacement and damage index. The polynomial-based response surface model is subsequently validated with a large number of simulations based on Latin Hypercube sampling. Results confirm the high accuracy of proposed technique in material uncertainty quantification.

show abstract

Response Surface Method of Reliability Analysis and its Application in Slope Stability Analysis

Cited by 37 publications

References 20 publications

Data-driven reduced order model and simplicial homology global optimization for reliability analysis and application

Data-driven reduced order model and simplicial homology global optimization for reliability analysis and application

An Improved Monte Carlo Method Based on Neural Network and Fuzziness Analysis: A Case Study of the Nanpo Dump of the Chengmenshan Copper Mine

Response Surface Method for Material Uncertainty Quantification of Infrastructures

Contact Info

Product

Resources

About