Dynamic Monitoring And Intelligent Early Warning Of Deep Foundation Pit Deformation Based On Point Cloud Semantic Recognition

Deformation monitoring and early warning of deep foundation pits is an important work content to ensure the safety of deep foundation pit construction,and a perfect monitoring and early warning system plays a vital role in avoiding casualties and major economic losses caused by the occurrence of deep foundation pit deformation accidents.However,at present,most of the foundation pit monitoring relies on local measurement points and manual inspection,and there is not enough means for judging the state of foundation pit and a lack of effective process control means.Therefore,based on the use of laser scanning technology to obtain complete 3D point cloud data of deep foundation pit scene,this paper carries out multisource sensing data processing and proposes early warning methods through deep learning,RANSAC and other algorithms,aiming to establish a more comprehensive deep foundation pit deformation monitoring system,realize dynamic monitoring and intelligent early warning,and improve the management efficiency and digitalization of deep foundation pit.This paper firstly verifies the feasibility and reliability of using laser point cloud for deep foundation monitoring by analyzing the specification requirements and error control,designs a laser point cloud monitoring scheme and obtains a complete three-dimensional point cloud monitoring data with a unified coordinate system with the traditional monitoring method,which lays the data foundation for the later.Subsequently,in order to improve the efficiency of point cloud data utilization and give semantic meaning to scattered point clouds for practical applications,a deep learning algorithm is used for semantic recognition of point clouds for deep foundation pit scenarios.In order to solve the problem of no open source point cloud database for deep foundation pit,with the analysis of structural classification of deep foundation pit scenes,this paper establishes a deep foundation pit point cloud database manually,which meets the data requirements for deep learning algorithm training.Through the concept of data block,learning window,and key feature point KNN linkage proposed,the data processing of large volume point clouds of outdoor scenes is made compatible with the adaptation of Pointconv neural network,and the intelligent semantic recognition and extraction of single structure of point clouds of deep foundation pit sites is realized.Secondly,parametric modeling of point cloud data and extraction of deformation parameters are realized by processing means of slicing,RANSAC data fitting and feature point extraction of the single point cloud structure.Combined with traditional monitoring data for fusion analysis,it enriches the form of deformation data needed for deep foundation pit monitoring and improves the data structure with a more complete data expression.In addition,by analyzing the structural form of IFC standard,the parametric model is converted and combined with the requirements of deep foundation pit deformation monitoring to complete the expansion of IFC standard,and finally the BIM management platform is built to realize the visualization and digitalization of deep foundation pit deformation monitoring.Finally,this paper establishes a deep foundation pit deformation accident early warning knowledge base by identifying deep foundation pit hazard sources and using accident tree analysis method as a logical basis,further early warning level grading criteria and response strategy under the corresponding level are proposed based on improved LEC evaluation method.A complete deep foundation pit monitoring,early warning and response system is finally established.Among them,the observable instability states applied by the early warning knowledge base are closely linked to the multi-source perceptual deformation data obtained from the previous processing,providing quantitative indicators for the abstract hazard sources and early warning concepts.The results show that the deep foundation pit deformation monitoring means and data processing methods based on point cloud data proposed in this paper can effectively improve the efficiency and data comprehensiveness of deep foundation pit monitoring,which is conducive to accurately grasp the dynamic safety state of foundation pits.The BIM management platform based on point cloud reconstruction in this paper provides a more efficient digital management means for deep foundation pit monitoring,and the established early warning and deformation response system is well established,which can provide a useful reference for deep foundation pit deformation monitoring.