چکیده مقاله
This paper utilizes a novel deep learning architecture Optimizer Net― specifically developed for structural optimization, to extract informative feature maps as part of an automated optimization process Addressing key challenges such as high computational cost, complex parameter tuning, and convergence issues, Optimizer Net transforms normalized energy data into contour or energy images that, together with optimized structures, serve as training input The architecture consists of 13 carefully designed layers, including convolutional, max pooling, fully connected, transposed convolutional, and upsampling layers These are further enhanced with batch normalization, leaky ReLU activation, dropout, and padding to ensure stable and efficient learning Central to the model's success is its ability to extract and refine high quality feature maps, which play a crucial role in capturing latent structural patterns within the energy contours Performance evaluation using Mean Squared Error MSE demonstrates superior accuracy and optimization efficiency compared to conventional methods
کلیدواژهها
نویسندگان
Masoomeh Arobli
Department of Civil Engineering, University of Tabriz, Tabriz, Iran
Nasser Taghizadieh
Department of Civil Engineering, University of Tabriz, Tabriz, Iran
Ali Hadidi
Department of Civil Engineering, University of Tabriz, Tabriz, Iran
سامان یغمایی سابق
Department of Civil Engineering, University of Tabriz, Tabriz, Iran
شیوه ارجاع
Arobli, Masoomeh and Taghizadieh, Nasser and Hadidi, Ali and Yaghmaei-Sabegh, Saman,1404,Topology optimization utilizing deep learning technique with an emphasis on feature extraction,14th International Congress on Civil Engineering,Tehran
ارائهشده در
مجموعه مقالات چهاردهمین کنگره بین المللی مهندسی عمران29 مهر 1404 · تهران