Application Research On Deformation Prediction And Control Method Of Deep Foundation Pit Engineering Based On Intelligent Algorithm

Deep foundation pit is a complex and important project.Its stability is not only related to the safety of the superstructure,but also to the deformation of surrounding buildings.In the process of foundation pit excavation,deformation may occur due to various factors,and its deformation is difficult to calculate by mechanical model or unified empirical formula.This paper uses and compares the finite element method and the system analysis method to analyze the advantages and disadvantages of the two methods for data prediction.The finite element simulation calculates various deformations of foundation pits such as surface settlement around the foundation pit.The foundation pit deformation cloud map can obtain the basic characteristics and trends of vertical deformation of the soil surrounding the foundation pit,but the accuracy is affected by the surrounding condition data.A BP neural network prediction model for vertical deformation of soil around the foundation pit is established.The prediction model based on the historical data of the time series is used to predict the future data.The accuracy of the prediction result is affected by the length of the detection data sequence.The BP neural network method is superior to the finite element method in calculation speed,and the BP neural network method can construct the model very conveniently.However,the BP neural network method can only be modeled by actual data in the process of implementation,so it is impossible to clearly express the deformation mechanism of the soil.The finite element method can effectively reflect the characteristics and development trend of soil deformation.Based on the monitoring data of the foundation pit engineering of a sewage station in Guangzhou,the finite element method calculates the calculated value of the horizontal displacement and settlement of the foundation pit at the end of each working condition,compared with the actual measurement,the relative error is 27.09% and 32.62%.In the calculation of engineering data,the horizontal displacement position is controlled at 30% and the settlement control is at 40%.The calculation results can meet the engineering needs.Compared with the finite element method,the BP network has a more accurate prediction result of the foundation pit deformation,and the average relative error is 9.57% and 7.07%.Use the measured data to make corrections,feedback to the design and construction,provide design basis for reducing engineering cost indicators;timely understand the impact and extent of the construction environment underground soil layer,underground pipelines,underground facilities,ground buildings in the construction process,timely adjustment construction Progress;timely discover and forecast the occurrence of dangerous situations and the development degree of dangerous situations,avoid safety accidents during construction,and effectively protect the stability of surrounding buildings and ensure construction qualityUnder the condition that the monitoring data can be continuously obtained,an intelligent control method combining neural network and fuzzy control is proposed.Through the implementation of predictive control,the continuous deformation monitoring of key parts around the foundation pit is carried out,and the monitoring information is timely fed back to the predictive control system.According to the feedback information,the system predicts the deformation during the next construction process,and automatically gives the construction parameter adjustment instruction according to the predicted deformation,and controls the construction deformation within the specified index value to realize the real information construction.The fuzzy controller has the advantages of simplicity,practicality,easy expansion and modification,and is convenient for real-time control.From manual monitoring to informational forecasting and control construction guidance,it can save manpower,materials,machinery and other engineering costs;at the same time,avoid engineering accidents,ensure construction safety,reduce economic losses,effectively improve project management efficiency,and achieve economic and social value.