Epistemic Uncertainty Treatment Using Group Method of Data Handling Algorithm in Seismic Collapse Fragility
| Autor: | Meisam Veghar, Fooad Karimi Ghaleh jough, S.Bahram Beheshti-Aval |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Earthquake engineering
Accuracy and precision Epistemic uncertainty Computer science Group method of data handling Aleatory uncertainty Monte Carlo method Aerospace Engineering 020101 civil engineering Ocean Engineering 02 engineering and technology 0201 civil engineering Fragility 0203 mechanical engineering General Materials Science Limit state design Uncertainty quantification Civil and Structural Engineering Mechanical Engineering Group Method of Data Handling (GMDH) algorithm Power (physics) 020303 mechanical engineering & transports Mechanics of Materials Automotive Engineering Algorithm |
| Zdroj: | Latin American Journal of Solids and Structures, Volume: 18, Issue: 3, Article number: e355, Published: 09 APR 2021 Latin American Journal of Solids and Structures v.18 n.3 2021 Latin American journal of solids and structures Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM) instacron:ABCM |
| Popis: | Developing fragility functions is the essential step in incorporating important uncertainties in next-generation performance-based earthquake engineering (PBEE) methodology. The present paper is aimed to involve record-to-record variability as well as modelling uncertainty sources in developing the fragility curves at the collapse limit state. In this article, in order to reduce the dispersion of uncertainties, Group Method of Data Handling (GMDH) in combination with Monte Carlo Simulation (MCS) is used to develop structural collapse fragility curve, considering effects of epistemic and aleatory uncertainties. A Steel Moment Resisting Frame (SMRF) is chosen as the tested structure. The fragility curves obtained by the proposed method which belongs to GMDH approaches are compared with those resulted from simple and well-known available methods such as First-Order Second-Moment (FOSM), Approximate Second-Order Second-Moment (ASOSM) and Monte Carlo (MC)/Response Surface Method (RSM), somehow, as an accurate method. The results of the application of the proposed approach indicate increasing accuracy and precision of the outputs as well as power with the same computational time compared to aforementioned methods. The GMDH method introduced here can be applied to the other performance levels. |
| Databáze: | OpenAIRE |
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