Scholarlink

<p>This study presents an approach that demonstrates the capabilities of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) in solving mechanics-related problems, particularly in the design of monolithic reinforced concrete slabs on a base. For the first time, a voxel-based representation of the studied object is proposed. In many cases, the design stage involves the inclusion of technological holes of various shapes, and the slab surface may have complex geometry. Determining the stress–strain state (SSS) using closed-form solutions under such conditions is highly labor-intensive or even unattainable. This paper presents an alternative approach using a 3D CNN with a U-Net architecture, Deep Convolutional Generative Adversarial Nets (3D-DCGAN), and an Improved GAN (I-GAN). This method enables accurate prediction of shrinkage stresses and displacements in slabs more efficiently than the finite element method (FEM). The paper highlights the promising potential of neural networks in structural engineering.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

3D-DCGAN and 3D-CNN-U-Net for predicting shrinkage stresses and displacements in monolithic reinforced concrete slabs on a base

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月8日

查看全文
  • A. E. Zheltkovich,
  • Yiqian He,
  • D. E. Marmysh,
  • Yuhang Ren,
  • V. V. Molosh,
  • Nan Mou,
  • Zien Huang,
  • Xiaoxia Guo,
  • P. I. Statkevich,
  • K. G. Parchotz

摘要

This study presents an approach that demonstrates the capabilities of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) in solving mechanics-related problems, particularly in the design of monolithic reinforced concrete slabs on a base. For the first time, a voxel-based representation of the studied object is proposed. In many cases, the design stage involves the inclusion of technological holes of various shapes, and the slab surface may have complex geometry. Determining the stress–strain state (SSS) using closed-form solutions under such conditions is highly labor-intensive or even unattainable. This paper presents an alternative approach using a 3D CNN with a U-Net architecture, Deep Convolutional Generative Adversarial Nets (3D-DCGAN), and an Improved GAN (I-GAN). This method enables accurate prediction of shrinkage stresses and displacements in slabs more efficiently than the finite element method (FEM). The paper highlights the promising potential of neural networks in structural engineering.