A new unbiased metamodel method for efficient reliability analysis
| Autor: | Hongzhe Dai, Guofeng Xue, Hao Zhang, Wei Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Scheme (programming language)
Computer science ComputingMethodologies_SIMULATIONANDMODELING 020101 civil engineering 02 engineering and technology Kriging metamodel 0201 civil engineering 0203 mechanical engineering Computer Science::Computational Engineering Finance and Science Approximation error Software_SOFTWAREENGINEERING Metamodelling Statistics Safety Risk Reliability and Quality Reliability (statistics) Civil and Structural Engineering computer.programming_language Statistics::Applications Markov chain Computer Science::Software Engineering High-dimensional model representation Building and Construction Reliability Statistics::Computation Term (time) Metamodeling 020303 mechanical engineering & transports 090506 - Structural Engineering [FoR] Unbiased estimation Adaptive refinement computer Algorithm Markov chain simulation |
| Popis: | Metamodel method is widely used in structural reliability analysis. A main limitation of this method is that it is difficult or even impossible to quantify the model uncertainty caused by the metamodel approximation. This paper develops an improved metamodel method which is unbiased and highly efficient. The new method formulates a probability of failure as a product of a metamodel-based probability of failure and a correction term, which accounts for the approximation error due to metamodel approximation. The correction term is constructed and estimated using the Markov chain simulation. An iterative scheme is further developed to adaptively improve the accuracy of the metamodel and the associated correction term. The accuracy and efficiency of the new metamodel method is illustrated and compared with the classical Kriging metamodel and high dimensional model representation methods using a number of numerical and structural examples. National Natural Science Foundation of China Australian Research Council |
| Databáze: | OpenAIRE |
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