Research On Intelligent Approach To Monitoring Safety In Hoisting Prefabricated Building Components

The hoisting of components is the most frequent,and very dangerous operation during prefabricated construction,in which safety accidents such as stuck by object happen frequently.At present,the research and practice in the safety management of prefabricated construction mainly focuses on the quantitative analysis of risks and on-site safety level assessment,but lacking the monitoring of safety risks in construction processes,and less focusing on specific operation scenarios.Meanwhile,the rapid development of Internet of Things and computer vision technology provides a powerful solution for on-site safety monitoring,this can effectively replace the traditional manual inspection.However,existing research and practice mainly focuses on the monitoring of single unsafe scene in construction sites,less considering the continuity of operation processes and the logical relationship between different scenes,particularly the safety monitoring of prefabricated construction scenes.This research aims at proposing an intelligent approach to monitoring safety in hoisting prefabricated building components.Firstly,based on the processes and features of prefabricated building components hoisting,this research systematically summarizes the potential safety risks and corresponding safety management requirements in standards,and selects the unsafe scenes as monitoring objects from the perspective of in-process control.Then,the hoisting processes are divided into scenes and well defined,and the continuous detection frameworks of unsafe scenes of precast wall and precast superposed slab are established respectively as examples with the establishing requirements proposed.Furthermore,aimed at the recognition of specific unsafe scenes in the continuous detection framework,the computer vision-based intelligent recognition methods of unsafe scenes are established.Finally,vertical support erection and precast wall hoisting are taken as examples to verify the recognition methods above.The results show that: 1)In the test of supporting scene safety recognition,the mean Average Precision of the model for detecting precast walls and inclined supports is up to 95.3%,the accuracy of the recognition method is 70.7%,and the detection speed is approximately 10 fps.2)In the test of hoisting scene safety recognition,the errors of the wall elevation calculation over the operation surface as well as the horizontal distance calculation between workers and components are 35.4mm and 112.2mm respectively,and the miss rate of the wall object is 16.9%;The overall accuracy of the recognition method and the accuracy under the condition of wall being successfully detected are 77.7% and 93.5% respectively,and the detection speed is around 10 fps.In summary,the above test results indicate that the unsafe scene recognition methods are effective,being able to meet practical requirements in construction sites in terms of distance calculation accuracy,real-time detection and unsafe scene recognition accuracy.This intelligent approach to monitoring safety in hoisting prefabricated components provides not only a new idea for realizing multi-scene persistent safety monitoring during the process of hoisting,but also a valuable reference for the safety monitoring of other operation processes,thus enriching the theory and method of construction safety management.Moreover,this method can effectively recognize the unsafe scenes during the process of component hoisting,thus providing supports for safety management decision and reducing construction safety accidents.Hence,this research is with the significance of both theory and practice.