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This research develops a highly effective deep-learning-based surrogate model that can provide the optimum topologies of 2D and 3D structures. In general, structural topology optimization requires plenty of computations because of a large number of finite element analyses to obtain optimal structural layouts by reducing the weight and satisfying the constraints. Therefore, many researchers have developed a deep-learning-based model using the initial static analysis results to predict the optimum designs. However, these studies still considered relatively simple example problems, such as a cantilever plate and MBB beam, even though they required a large number of data to achieve an accurate surrogate model for the simple application. To overcome these limitations, we propose a new framework, which 1) efficiently uses limited data by normalizing it and 2) employs a surrogate model capable of handling more …