Application study of deep learning algorithm for steel structure shelves deformation prediction
With the growth of industrialization and the logistics industry,steel structure shelves have become increasingly vital in storage and logistics systems,making accurate deformation prediction essential.This paper presents a prediction algorithm based on Bidirectional Long Short-Term Memory Networks(BiLSTM)and Attention mechanisms.Utilizing time series data and training through deep learning models,this algorithm allows for detailed analysis and prediction of deforma-tions in steel structure shelves.The model's performance,validated by a typical application,shows high robustness and exceptional predictive accuracy.Experimental results indicate that the model can accurately predict deformations in steel structure shelves,with an average error of only 0.15%to 3.33%.These outcomes suggest potential applications of this algorithm in the automated monitoring of steel structure shelves,providing a viable solution for predicting structural de-formations.
automated monitoring technologydeep learningtime series dataBidirectional Long Short-Term Memory Networks with an Attention mechanism(BiLSTM-Attention)
万方数据
维普
