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Structural Engineering and Mechanics Volume 80, Number 6, December25 2021 , pages 711-726 DOI: https://doi.org/10.12989/sem.2021.80.6.711 |
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Multiparameter recursive reliability quantification for civil structures in meteorological disasters |
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Vincent Z. Wang and Sam Fragomeni
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| Abstract | ||
| This paper presents a multiple parameters-based recursive methodology for the reliability quantification of civil structures subjected to meteorological disasters. Recognizing the challenge associated with characterizing at a single stroke all the meteorological disasters that may hit a structure during its service life, the proposed methodology by contrast features a multiparameter recursive mechanism to describe the meteorological demand of the structure. The benefit of the arrangements is that the essentially inevitable deviation of the practically observed meteorological data from those in the existing model can be mitigated in an adaptive manner. In particular, the implications of potential climate change to the relevant reliability of civil structures are allowed for. The application of the formulated methodology of recursive reliability quantification is illustrated by first considering the reliability quantification of a linear shear frame against simulated strong wind loads. A parametric study is engaged in this application to examine the effect of some hyperparameters in the configured hierarchical model. Further, the application is extended to a nonlinear hysteretic shear frame involving some field-observed cyclone data, and the incompleteness of the relevant structural diagnosis data that may arise in reality is taken into account. Also investigated is another application scenario where the reliability of a building envelope is assessed under hailstone impacts, and the emphasis is to demonstrate the recursive incorporation of newly obtained meteorological data. | ||
| Key Words | ||
| incomplete data; multiparameter formulation; natural hazards; probability; recursion; structural safety | ||
| Address | ||
| Vincent Z. Wang and Sam Fragomeni: College of Engineering and Science, Victoria University, Melbourne, VIC 3011, Australia | ||