Prediction And Warning Method,Program Realization And Application Of Deep Foundation Pit Deformation Based On Intelligent Algorithm

At present,foundation pit accidents are still high in engineering,especially for deep foundation pit projects.By analyzing the accident,it is found that the lack of attention to the monitoring and early warning of foundation pit deformation is an important cause of the accident.The foundation pit engineering situation is complex,and there are many factors that affect the foundation pit deformation: the heterogeneity of rock and soil materials,the depth and aspect ratio of the foundation pit,the on-site rainfall and construction rate and other factors will affect the deformation and stability of the foundation pit.The reason for the poor effect of traditional monitoring and early warning methods is that it is difficult to consider all the influencing factors,and it is difficult to achieve real-time monitoring.With the development of computer technology,the realization of high-precision real-time monitoring and early warning of foundation pit deformation has become a Possibly,this will have important practical significance for the safety management of foundation pit engineering.This paper takes a deep foundation pit project in Nanshan District,Shenzhen as a research example,and establishes a set of foundation pit deformation monitoring and early warning system based on intelligent algorithms.The whole system is composed of an algorithm package with three modules as foundation pit deformation monitoring,deformation prediction and safety early warning.The intelligent algorithm is implemented by Python language programming.By combining monitoring,forecasting and early warning,it progresses layer by layer.Based on the monitoring data of each point in the time dimension,it realizes the prediction and trend analysis of the deformation of each point in the future.The safety and stability of the foundation pit are considered at each level,which not only ensures the safety of each monitoring point,but also realizes the control of the overall deformation of the foundation pit.(1)In the foundation pit monitoring module,the missing values of the monitoring data collected on the spot are firstly processed by linear interpolation,and then according to the principle that the deformation of the monitoring points in the same monitoring project is consistent,the gray correlation method is used to analyze the monitoring data.The correlation analysis is carried out,and the change of the correlation degree of the monitoring points is realized by the dynamic rolling method.With 10-12 monitoring data as a sequence,and 3-5data iterations as a step,the deformation trend of the foundation pit can be analyzed according to the region.It is convenient to check and replace abnormal monitoring sensors and reduce engineering risks.(2)In the deformation prediction module,two prediction methods,unequal time interval gray model and convolutional neural network,are used to achieve high-precision prediction of foundation pits.The univariate grey model prediction method GM(1,1)is used in the early stage of the project or when the monitoring point data is less.When the amount of data is large in the later stage of the project,a one-dimensional convolutional neural network(CNN)is used for prediction.Both methods are implemented by the dynamic rolling prediction method,which uses 7 data as a sequence to predict the deformation,and sets the dynamic rolling prediction step size to 1.In addition to completing the deformation prediction of each point,it is also necessary to carry out error analysis on the prediction results,and select a method with higher accuracy for prediction through the continuously collected error conditions.(3)In the safety early warning module,the collected monitoring data is used to judge the overall deformation of the foundation pit by the method of data fusion,and then the stability of each monitoring point is judged according to the safety grading table of the monitoring points of the foundation pit,and then Use the Analytic Hierarchy Process(AHP)to build a foundation pit safety early warning framework,and analyze the monitoring point data step by step to obtain the overall stability of the foundation pit,but it is not enough to obtain the qualitative evaluation of the stability of the foundation pit.Combining AHP with the concept of membership function in fuzzy mathematics,a new measure for quantitative evaluation of foundation pit stability is proposed.This paper takes the foundation pit engineering as the research object,uses artificial intelligence and applied mathematics as the method,and uses the computer programming language Python to make a detailed analysis and discussion on the deformation,safety and stability of the foundation pit,which provides practical and feasible on-site management of the foundation pit engineering in the future.The new method is of great practical significance for avoiding engineering risks and ensuring engineering quality.