Classification and early warning research of scaffolding hidden trouble based on multi-source information fusion and WOA-CNN-LSTM
In light of the information diversity of hidden dangers of external scaffold at construction sites,the traditional single-signal early warning research based on sensor monitoring has the problems of poor fault tolerance and limited information.Aiming at the"image+monitoring"data of external scaffold at construction site,this study proposes a classification and early warning method of hidden scaffold dangers based on the information fusion at data layer and feature layer.Firstly,the solid model of the floor type double row fastener steel tubular scaffold was established by using the Revit 3D modeling software,and its safety was reviewed.Then,the finite element analysis of the scaffold under different hidden danger conditions was carried out by using the random sampling method,and the displacement monitoring data and image data of hidden dangers were divided into four types of early warning levels respectively.Secondly,the Unscented Kalman Filter(UKF)algorithm was used to denoise and fuse the same kind of information from multiple sources to form a kind of heterogeneous information.Besides,the Convolutional Neural Network-Long Short Term Memory Network(CNN-LSTM)was used to fuse the"image+monitoring"data at the feature level.Finally,through the real-time collection of the monitoring data of the floor type double row fastener steel tubular scaffold of a project under construction in Xi'an,the hidden danger information of the scaffold was classified for early-warning.The Whale Optimization Algorithm(WOA)was used to optimize the parameters of the CNN-LSTM network,When the number of hidden nodes is 30,the learning rate is 0.007 2,the regularization coefficient is 1 x 10-4,and the classification effect is the best.After optimization,the early warning accuracy reaches 91.452 6％.Through visualization of WOA-CNN-LSTM,CNN-LSTM,CNN-SVM(Support Vector Machine)and CNN-GRU(Gate Recurrence Unit)classification and early warning results,the superiority of optimized CNN-LSTM network in scaffold classification and early warning is confirmed.
safety engineeringmulti-source information fusionWhale Optimization Algorithm(WOA)Convolutional Neural Network-Long Short Term Memory Network(CNN-LSTM)visualization
NETL
NSTL
万方数据
维普
