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In nonlinear cases, the SDG method requires multidimensional search procedures. However, in linear cases only one-dimensional search procedures are required to identify the critical gust load conditions. In this study the application of the backpropagation ANN method as a multi-dimensional modeling tool has been proposed to model or identify the global and local extrema of one-dimensional gust load responses. The maximum and minimum response values of ramp-step input gust profiles were considered to investigate the ANN modeling capability and effectiveness. The actual SDG analysis for nonlinear cases was hypothesized to be performed over a large and sparse domain, therefore the ANN could be trained to quickly identify the region of the domain containing the global extrema. The SDG analysis, then, could be concentrated on a smaller region thereby reducing computation time.