Research On The Mechanism And Intelligent Recognition Of Unsafe Hoisting Behaviors For Tower Crane

Tower cranes are one of the most used machinery in construction activities.However,tower crane safety accidents still occur frequently at home and abroad,causing serious casualties and economic losses.A large number of accident investigation reports have shown:unsafe hoisting behaviors are the fuse of tower crane safety accidents.For example,in Guiyang and Gansu.China,tower cranes broke off due to unsafe hoisting behaviors.How to get the influence effect of unsafe hoisting behaviors on the stability of tower cranes,establish its intelligent recognition model,and realize the operation process control of hoisting operators.It has become an important topic for tower crane safety management.This thesis summarizes the most typical unsafe hoisting behaviors(tilt hoisting,sudden braking,sudden unloading)and the accident type(independent & pointed tower crane)through accident cases.Taking the above type as an example,a tower crane experimental platform based on a scale model is established,to avoid the danger of on-site experiment.Then,this thesis systematically studies unsafe hoisting behaviors,including instability effects,monitoring parts seclection,intelligent models,and scene generalization application.In the dynamic mechanism of unsafe hoisting behaviors.The load excitation effect of the above unsafe hoisting behaviors is summarized.Then the collapse level of the tower crane is constructed,and the elastoplastic changes of each component under different hoisting behaviors are discussed based on the incremental dynamic analysis method.The results show that tilt hoisting is most likely to cause tower crane instability.The Pearson correlation of the damage evolution between the bottom mast and the foundation is more than 0.9.And this mast is the most sensitive to the classification of different hoisting behaviors and can be used as the key part of the sensor layout.In the intelligent recognition method of unsafe hoisting behaviors.This thesis establishes a labeled image feature data set of unsafe hoisting behaviors for the first time through experimental research,which contains a total of 16200 image data and covers the rotation attitude of-90 ° to 90 °.An image data fusion method based on RGB channels is proposed,and an intelligent recognition model of the unsafe hoisting behavior is established.This thesis compares the recognition benefits of different feature extraction methods(Rawdata,GADF,CWT)and different deep learning frameworks(Alex Net,Vgg Net,Res Net,Google Net,Mobile Net)in detail.The Rawdata-Res Net is the optimal recognition path of the unsafe hoisting behavior,and its recognition accuracy reaches 99.90%.In the construction site application of the recognition model.This thesis proposes a generalized idea of the intelligent recognition model based on deep transfer learning.It has realized the application transition from the scaled model to the full-scale model of the tower crane,avoiding the problem of less monitoring data of unsafe hoisting behaviors with labels on construction sites.According to the experimental data on-site,the proposed transfer model can overcome the "negative migration" problem caused by the lack of label type information in the target domain data set,and the optimal accuracy reaches 76.74% with the validation set.Finally,an offline functional module for intelligent recognition of the unsafe hoisting behavior is designed through Qt Designer.