Epistemic Uncertainty Treatment Using Group Method of Data Handling Algorithm in Seismic Collapse Fragility

• Autor(a) principal: jough,Fooad Karimi Ghaleh
• Data de Publicação: 2021
• Outros Autores: Veghar,Meisam, Beheshti-Aval,S.Bahram
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Latin American journal of solids and structures (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1679-78252021000300501
• topic: Group Method of Data Handling (GMDH) algorithm; Monte Carlo method; Aleatory uncertainty; Epistemic uncertainty

Abstract 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.