Analysis of Seismic Elastic Steel Structures Using a New Hybrid Optimization Deep Learning Model - Li, Ping; Liu, Wanfeng; Yan, Tiecheng (IEEE access, 2025)
In response to the insufficient performance of traditional seismic elastic analysis models for steel structures, this study proposes a deep layered feature learning network model in a mixed optimization point set metric space by combining cross attention mechanism, graph neural network, and random sampling consistency algorithm. The experiment showed that the average F1 score of this model in cloud matching tasks was 0.919, which was 16.5%-22.6% higher than traditional methods. Moreover, the matching success rate (55.43%) was still higher than the comparison method by more than 15% at a 50% occlusion rate. In semantic segmentation tasks, the intersection to union ratio (0.798) and Dice coefficient (0.879) of steel pipe components were higher than traditional methods. In actual scenario testing, the model achieved sub-millimeter level deformation detection (the minimum deformation detection amount for steel pipes and welded balls was 0.44mm and 0.31mm). The generalization error (0.06mm) and memory peak value (11.3GB at 10K points) were reduced by 76.9% and 53.9% compared to the comparison method, which is suitable for real-time monitoring needs of edge devices. In summary, the proposed model addresses the shortcomings of traditional methods in dynamic interaction, noise suppression, and deformation sensitivity through multimodal feature fusion and hybrid optimization strategies. This provides a high-precision and low-energy solution for seismic analysis of complex steel structures.