Research On Excavation Deformation Forecasting Methods Of Subway Foundation Pit And Its Application

Deep excavation engineering construction will produce pit supporting structure displacement, foundation-pit bottom uplift, the surrounding ground settlement and etc, big deformation will cause instability of foundation pit or crack of the surrounding structures and other engineering accidents. There are many factors influence the deep foundation pit deformation, which belong to the typical nonlinear problems. According to the need of informatization construction, construction monitoring must be carried out in the process of deep foundation pit engineering construction, and deformation forecast must be made according to the monitoring datas of foundation pit. This paper is taking deep foundation pit engineering of Yan Tang station of No.3 Guangzhou metro line for example (so far the largest deep excavation engineering in guangzhou area). Finite element method is used to analyze the deformation caused by deep foundation pit excavation construction, a predicting model is constructed based on grey system, markov chain and artificial neural network to analyze various deformation forecast model. The applicability of various model is made through actual test and verify check. The main works in this paper are as follows:(1) The properties and difficulties of the deep excavation construction of Yantang station were analyzed. The construction scheme and the monitor scheme of open-cut part of deep foundation pit of Yantang subway station were made. The supporting scheme underground continuous wall combined with inner supporting are adopted. The deformation of the deep foundation pit is monitored continuously during the whole construction.(2) Three dimensional mathematical analyze was carried out on the excavation of the deep foundation pit of Yantang subway station. The model's size effect and space effect were deeply studied and the final dimensions of the model were confirmed. The analysis result of piles and walls'horizontal displacement, supporting axis strength and the surrounding earth surface deformation fit well with in-situ survey value. The effects of construction working procedure and each working step to deformation of the deep excavation were analyzed. According to the contrast analysis, the calculation results fit well with actual project results by using rigidification soil model and setting piles-soil interface contact element to the calculation model.(3) The modeling applicability of the metabolism model was verified. Metabolism GM (1,1) model and gray markov chain models for short-term forecasting were established, short-term prediction of the deformation of foundation pit was done. The results show that predict accuracy from which the short-term forecast metabolism GM (1.1) model and gray markov chain models predict can meet the requirements which the construction need and grey markov chain model can be firstly considered.(4) For medium and long-term forecast, metabolism residual GM (1,1) forecasting methods and gray markov chain residual forecasting method were established. The deformation forecast values for medium and long term development was predict by using these methods. The predicted results show that the prediction accuracy of the two models both can satisfy the demand of the construction need, but gray markov chain residual model was more appropriate.(5) Factors which affect the deformation of deep foundation pit were analyzed.7 factors was chosen to be the effect factors for BP neural network predict model. They are the soil internal friction angle, soil cohesion force, unit weight of soil, groundwater table, the permeability coefficient, excavation depth of deep foundation pit, number of interior support layers. The BP network construction modeling methods based on time sequence and based on each influence factor are discussed respectively. The deformation prediction was made by using the models, the prediction precision of the models are good.(6) Comparison analysis among the grey markov forecast model, gray markov residual model, BP neural network model based on time sequence, BP neural network model based on the influence factors were carried out. The values from analysis forecasting model are fit well with the in-situ monitor values and all the models can be applied for deformation dynamic prediction of deep foundation pit during construction. The calculating values from the BP neural network model based on each effect factor most closely meet the field monitoring values. The prediction accuracy of BP network model is slightly higher than gray markov model.