Research On Foundation Pit Deformation Prediction Model Based On Dynamic Monitoring And Feedback Warning System Of Internet Of Things

With the revolutionary development of information technology in recent years,5G communication networks,remote monitoring technology,and smart devices have gradually taken away from human life,marking the coming of the Internet of Things era.At present,the smart home,medical,transportation,energy and environmental protection,agricultural production,industrial manufacturing and other fields have achieved some results in the Internet of Things.In the field of foundation pit engineering,sensor automation monitoring technology has also been widely used,and the dynamic data of engineering is effective.Collection is no longer a problem,but there is no progress in the processing and analysis of monitoring big data and the development of an intelligent feedback system for the Internet of Things.In order to keep up with the requirements of the times,and in response to this problem,this paper based on the underground civil air defense engineering project of Shenyang North Station,based on indoor geotechnical tests,multi-dimensional dynamic monitoring tests and a variety of intelligent prediction theories,carried out a prediction model of foundation pit deformation and objects Research on intelligent feedback and early warning system of networking +.The research work can be summarized as the following five aspects:(1)Conduct field follow-up investigation on the underground civil air defense engineering project of Shenyang North Station,fully understand the engineering geology,hydrogeology and surrounding environment of the project,and master the support plan and construction plan of the foundation pit.The basic physical and mechanical parameters of the rock and soil layer of the foundation pit were obtained by collecting soil samples on site and carrying out geotechnical experimental research.(2)Conduct a multi-dimensional dynamic monitoring test on the site of the foundation pit project of the underground civil air defense project in Shenyang North Station,and develop a detailed multi-dimensional joint dynamic monitoring program based on the scale,purpose,construction environment and surrounding environment of the underground civil air defense project in Shenyang North Station.The content mainly includes two major aspects: monitoring of foundation pits and surrounding structures and surrounding environment monitoring.Among them,the monitoring of foundation pits and enclosure structures includes: monitoring of settlement of upright piles,lateral earth pressure on the side walls of foundation pits,axial forces of supporting members,horizontal and vertical displacements of pile tops,and groundwater level outside the pits.The monitoring of the surrounding environment of thefoundation pit includes: surface settlement within two kilometers,settlement of surrounding buildings,cracks in buildings and deformation observation of underground pipelines.Obtained a large amount of monitoring data during the construction of the foundation pit project.(3)Explore the engineering application of artificial intelligence,in-depth study of the concept of factor space,numerical analysis and prediction principles and neural network theory,based on the classic neural network and foundation pit monitoring data,successfully predict the surrounding surface settlement,using monitoring data to The BP neural network model in the neural network theory was trained and successfully predicted the settlement of surrounding buildings.(4)Establish a predictive model of factor neural network.First,construct a model calculation coordinate system according to the model characteristics,and establish a factor space coordinate system based on the factor space theory,which can convert the foundation pit problem into a mathematical problem,and then the multi-dimensional factor space coordinate system can be quantitatively analyzed.According to the dimensional analysis method of the multi-dimensional factor space coordinate system,the number of final factor coordinate axes is determined,and the multi-dimensional factor space coordinate system that can analyze the foundation pit problem is established;then,based on the principle of matrix theory,the multivariate dynamic monitoring data is integrated,Find the vector matrix space of the monitoring data unit;based on BP artificial neural network theory,reconstruct the factor neuron network model,hoping that this model can complete the operation of big data based on the real-time monitoring data,and finally the future trend of the deformation of the foundation pit Make accurate predictions.(5)Based on the factor neural network prediction model,geotechnical mechanics model and multivariate information dynamic monitoring data,a quantitative information feedback system model was established.The system can quantitatively describe all internal and external factors that are subjected to dynamic construction,and always feedback the force and displacement of the foundation pit during construction,and can predict the future force and displacement of the foundation pit.Take the quantitative information feedback system model as the core module,build a hardware module with the multiple monitoring system equipment of the Internet of Things +,and develop supporting related software modules.Quantitative information feedback system module,Io T+ multi-monitoring system equipment,hardware module and open source software module work together to work together,which is the Io T+foundation pit deformation feedback early warning system.(6)The finite element software MIDAS is used to model and calculate the foundation pit,and the analysis results are compared with the calculation results of the factor-neural network prediction model.It is believed that the more data,the closer the calculation result of the factor-neural network prediction model is to the calculation result of the MIDAS software.