| Title: |
Stress prediction method for the main truss of launching gantry crane based on generative adversarial network |
| Authors: |
Yan, Guoping; Yang, Xiaowei; Li, Shihuang; Xiao, Lan; Li, Bazhou; Li, Haocheng |
| Contributors: |
2024 Wuhan Metropolitan Circle Collaborative Innovation Science and Technology Project of China; Wuhan Science and Technology Achievement Transformation Project; Hubei Provincial Technical Innovation Project; Hubei Province Support Project for Technological Innovation and Development of Enterprises (High-tech Enterprises) of China |
| Source: |
Engineering Research Express ; volume 8, issue 4, page 045106 ; ISSN 2631-8695 |
| Publisher Information: |
IOP Publishing |
| Publication Year: |
2026 |
| Description: |
As a critical equipment in the construction of cable-stayed bridge girders, the structural stress state of the launching gantry crane directly affects construction safety and efficiency. To achieve high-precision prediction of the main-truss stress, a stress prediction model based on an improved generative adversarial network was proposed in this study. The input of the model is a dataset of 128 × 128 resolution stress images established based on finite element simulation data, with the predicted stress distribution images of truss structures at the same resolution as the output. A supervised learning mechanism is introduced to enhance model stability and convergence speed. The generator adopts an improved U-Net architecture, and a self-attention (SA) mechanism is incorporated to optimize the discriminator’s performance. A hybrid objective function is used to achieve multi-angle error measurement and generation optimization. Comparative model training results demonstrate that the proposed SA-Def-GAN model outperforms DenseNet-CNN, U-Net-CNN, and FNN models. Experimental results demonstrate that the prediction errors of the SA-Def-GAN model, compared to finite element analysis, were 10.19%, 2.1%, and 3.6% under load conditions of 5 kg, 10 kg, and 15 kg, respectively. When evaluated against strain gauge measurements, the corresponding errors were 7.6%, 5.6%, and 8.1% for the same loading cases. These results collectively verify the model’s effectiveness and engineering applicability, providing a reliable technical means for real-time stress monitoring of launching gantry crane. |
| Document Type: |
article in journal/newspaper |
| Language: |
unknown |
| DOI: |
10.1088/2631-8695/ae455b |
| DOI: |
10.1088/2631-8695/ae455b/pdf |
| Availability: |
https://doi.org/10.1088/2631-8695/ae455b; https://iopscience.iop.org/article/10.1088/2631-8695/ae455b; https://iopscience.iop.org/article/10.1088/2631-8695/ae455b/pdf |
| Rights: |
https://publishingsupport.iopscience.iop.org/iop-standard/v1 ; https://iopscience.iop.org/info/page/text-and-data-mining |
| Accession Number: |
edsbas.A84BC4D2 |
| Database: |
BASE |