scholarly journals Structural Reliability Assessment by Integrating Sensitivity Analysis and Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Shao-Fei Jiang ◽  
Da-Bao Fu ◽  
Si-Yao Wu
Keyword(s):  
Sensitivity Analysis ◽  
Support Vector Machine ◽  
Structural Reliability ◽  
Reliability Assessment ◽  
Assessment Method ◽  
Reliability Theory ◽  
Performance Comparison ◽  
Support Vector ◽  
Performance Function ◽  
Small Influence

To reduce the runtime and ensure enough computation accuracy, this paper proposes a structural reliability assessment method by the use of sensitivity analysis (SA) and support vector machine (SVM). The sensitivity analysis is firstly applied to assess the effect of random variables on the values of performance function, while the small-influence variables are rejected as input vectors of SVM. Then, the trained SVM is used to classify the input vectors, which are produced by sampling the residual variables based on their distributions. Finally, the reliability assessment is implemented with the aid of reliability theory. A 10-bar planar truss is used to validate the feasibility and efficiency of the proposed method, and a performance comparison is made with other existing methods. The results show that the proposed method can largely save the runtime with less reduction of the accuracy; furthermore, the accuracy using the proposed method is the highest among the methods employed.

scholarly journals One-Class Support Vector Machine Based Schemes for Structural Reliability Assessment Under Imbalanced Sample Conditions

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 184350-184359 ◽  
Author(s):  
Li Zhan ◽  
Jie Liu ◽  
Mian Zhang ◽  
Chengning Zhou ◽  
Lin Zhang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Structural Reliability ◽  
Reliability Assessment ◽  
Support Vector

scholarly journals A Practical Nonprobabilistic Reliability Assessment Method on Key Strata Shear Failure in Longwall Mining

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hebing Luan ◽  
Jiachen Wang ◽  
Guowei Ma ◽  
Ke Zhang
Keyword(s):  
Structural Reliability ◽  
Shear Failure ◽  
Longwall Mining ◽  
Reliability Assessment ◽  
Friction Angle ◽  
Assessment Method ◽  
Mechanical Tests ◽  
Potential Hazard ◽  
Support Design ◽  
Key Strata

Roof cutting has long been a potential hazard factor in longwall panels in some diggings in China. Meanwhile, the key strata structural reliability, which provides an assessment on the stability of overlying roof strata, may be a significant reference for support design in underground coal mines. This paper aims to investigate a practical nonprobabilistic reliability assessment method on key strata. The mechanical tests and the hollow inclusion triaxial strain tests were conducted to measure relevant mechanical parameters and in situ stress. Furthermore, against the typical failure features in Datong Diggings, China, a shear failure mechanical model of key strata is proposed. Then, an allowable-safety-factor based nonprobabilistic stability probability assessment method is given. The sensitivity of geometrical dimensions and uncertainty levels of friction angle and cohesion are further studied. It is found that thickness and span of key strata have more dominative effect on key strata’s stability compared with the other factor and the increase of uncertainty levels results in decrease of stability probability.

scholarly journals Model Quality Assessment Method Based on Support Vector Machine

2021 ◽  
Vol 25 (1) ◽  
pp. 35-39
Author(s):  
Łukasz Glodek ◽  
Szymon Bysko ◽  
Witold Nocoń
Keyword(s):  
Support Vector Machine ◽  
Quality Assessment ◽  
Industry 4.0 ◽  
Assessment Method ◽  
Support Vector ◽  
Model Quality ◽  
Digital Twin ◽  
Model Quality Assessment ◽  
Virtual Commissioning ◽  
Quality Assessment Method

This paper proposes a model quality assessment method based on Support Vector Machine, which can be used to develop a digital twin. This work is strongly connected with Industry 4.0, in which the main idea is to integrate machines, devices, systems, and IT. One of the goals of Industry 4.0 is to introduce flexible assortment changes. Virtual commissioning can be used to create a simulation model of a plant or conduct training for maintenance engineers. On a branch of virtual commissioning is a digital twin. The digital twin is a virtual representation of a plant or a device. Thanks to the digital twin, different scenarios can be analyzed to make the testing process less complicated and less time-consuming. The goal of this work is to propose a coefficient that will take into account expert knowledge and methods used for model quality assessment (such as Normalized Root Mean Square Error – NRMSE, Maximum Error – ME). NRMSE and ME methods are commonly used for this purpose, but they have not been used simultaneously so far. Each of them takes into consideration another aspect of a model. The coefficient allows deciding whether the model can be used for digital twin appliances. Such an attitude introduces the ability to test models automatically or in a semi-automatic way.

Efficient Reliability Assessment With the Conditional Probability Method

2018 ◽  
Vol 140 (8) ◽  
Author(s):  
Rami Mansour ◽  
Mårten Olsson
Keyword(s):  
Conditional Probability ◽  
Structural Reliability ◽  
A Priori ◽  
Reliability Assessment ◽  
Probability Of Failure ◽  
Assessment Method ◽  
Probability Method ◽  
Probability Of Survival ◽  
Design Variables ◽  
Reliability Method

Reliability assessment is an important procedure in engineering design in which the probability of failure or equivalently the probability of survival is computed based on appropriate design criteria and model behavior. In this paper, a new approximate and efficient reliability assessment method is proposed: the conditional probability method (CPM). Focus is set on computational efficiency and the proposed method is applied to classical load-strength structural reliability problems. The core of the approach is in the computation of the probability of failure starting from the conditional probability of failure given the load. The number of function evaluations to compute the probability of failure is a priori known to be 3n + 2 in CPM, where n is the number of stochastic design variables excluding the strength. The necessary number of function evaluations for the reliability assessment, which may correspond to expensive computations, is therefore substantially lower in CPM than in the existing structural reliability methods such as the widely used first-order reliability method (FORM).

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