Design and stability evaluation of foundation pit support based on deformation monitoring results
Particle swarm optimization algorithm,generalized regression neural network,and differential autoregressive moving average model are used to predict the deformation of foundation pits.The stability of the foundation pit construction process is evaluated based on the prediction results to demonstrate the effectiveness of foundation pit support measures.The geological con-ditions and proximity conditions in the target excavation area are relatively complex,requiring the use of rigid support structures to control the deformation of the foundation pit.Therefore,pile anchor support structure is chosen as the form of foundation pit support.To ensure the economic efficiency of the foundation pit support system,zoning support measures are also designed.In the process of evaluating the stability of the foundation pits,the range of settlement deformation under current conditions is 12.68-21.42 mm,with an average of 17.84 mm.The range of horizontal displacement change is 15.08-22.49 mm,with an aver-age of 18.68 mm.It is found that both types of deformation are smaller than the deformation control value,indicating that the current deformation of the foundation pit is within the deformation control value range and has good stability.Through deforma-tion prediction,it is found that the subsequent deformation of the foundation pit has a convergence trend,and the range of subse-quent deformation values is 21.35-23.58 mm,which is also smaller than the deformation control value.This indicates that the sub-sequent deformation control of the foundation pit is also good,and so is the stability of the foundation pit.
foundation pitsupport designdeformation monitoringgeneralized regression neural networkdeformation predic-tion
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