Seismic resilience index for RC moment frames of school buildings using neuro-fuzzy approach

This article presents a neuro-fuzzy model to predict the resilience index of reinforced concrete moment frames of school buildings. For this purpose, several parameters, including the total height to total width ratio, period, spectral acceleration, peak ground velocity, the effective duration of the earthquake, and distance from the fault, have been considered. The database required to create the neuro-fuzzy model was gathered using the results of the nonlinear analysis of the frames. Based on this analytical data, an approach was introduced and formulated to predict resilience. Further, an existing school building was used to validate the proposed neuro-fuzzy model, and the values were compared. The results indicate a high level of accuracy in calculating the resilience index of the moment frames. In the available methods, it is necessary to perform a large number of time-consuming analyses and complex calculations to measure resilience. However, by using the proposed model, the resilience index could be determined without such analysis.