Research Methods for Reliability Analysis of Engineering Structures Based on Statistical Theory

Authors

  • Lining Wang

DOI:

https://doi.org/10.54097/hset.v28i.4066

Keywords:

Reliability analysis; probabilistic model; loads; structural resistance parameters; probability of failure.

Abstract

With the increasing need for construction engineering projects, the reliability problem of engineering structures has been the focus of public concern.Currently, there is a lack of a systematic summary of structural reliability theory. Therefore, this paper attempts to summarize some basic principles of structural reliability theory. This paper discusses various factors affecting reliability analysis and three typical analysis methods. It is pointed out that multiple factors affecting reliability analysis, such as the applying loads and resistance parameters such as material properties and dimension parameters, can be regarded as random variables and obey specific statistical distribution. First Order Reliability Method presents the most excellent convenience in calculating failure probability, especially for the structure with a relatively small, and the accuracy of this method is acceptable when the nonlinear degree of structural performance function is not significant.The Second Order Reliability Method could provide a relatively accurate solution with a more complicated calculation process. Monte Carlo Simulation is general enough to be applied to all the cases despite the considerable number of simulations resulting from a minuscule failure probability. Researchers are expected to implement improvement measures to simplify analysis methods by combining them with mathematics and computer science. The results of this paper can provide a theoretical reference for the researchers and designers of structural reliability theory.

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Published

31-12-2022

How to Cite

Wang, L. (2022). Research Methods for Reliability Analysis of Engineering Structures Based on Statistical Theory. Highlights in Science, Engineering and Technology, 28, 94-100. https://doi.org/10.54097/hset.v28i.4066