Abstract
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.
Highlights
Safety analysis of structures in seismic conditions is one of the significant phases of structural design especially in urban areas with high hazard of earthquake
This paper represents the efficiency of the Group Method of Data Handling (GMDH) Monte Carlo method against the probabilistic approach through applying methods to a case study Steel Moment Resisting Frame (SMRF) structure
It can be seen that while the results perfectly conform to those obtained using the Monte Carlo based logarithm method, consideredepistemic uncertainties made changes in mean and standard deviation value
Summary
Safety analysis of structures in seismic conditions is one of the significant phases of structural design especially in urban areas with high hazard of earthquake. Collapse of buildings may be triggered through the lack of load carrying capacity of lateral and gravity load resisting systems In this case LLS= LCOLLAPSE is mean annual frequency of collapse, which may be used as metric for safety assessment of structures against various probable intensity levels in site specific seismic hazard. Where the logarithmic standard deviation attributed to record-to-record uncertainties ranges between 0.35 and 0.45 depending on the structure of interest, the logarithmic standard deviation associated with modeling uncertainties may be as much as 0.45 (Haselton, 2006) Combining these uncertainties can make a significant impact on collapse probabilities, revealing the importance of incorporating modeling uncertainties in the seismic risk assessment. GMDH methodology is proposed to consider modelling uncertainties and to derive collapse fragility curves, incorporating epistemic and aleatory uncertainties, and it is compared with the other methods
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