Foundation pit displacement prediction method based on particle swarm optimization BP neural network

Abstract

The invention relates to a foundation pit displacement prediction method based on a particle swarm optimization BP neural network, and the method comprises the following steps: 1) selecting a first displacement monitoring point of a slope top of a foundation pit project and a plurality of adjacent monitoring points, and obtaining deformation monitoring data of the monitoring points in a past firsttime period as sample data; 2) performing preprocessing including data normalization on the sample data to obtain a training data set; 3) constructing a BP neural network; 4) optimizing the weight and threshold of the BP neural network by using a particle swarm algorithm, and outputting the optimal weight and threshold; 5) assigning the obtained optimal weight and threshold to a BP neural network, and then performing network training by adopting the training data set to obtain a trained BP neural network; and 6) predicting the displacement deformation of the first displacement monitoring point in the second time period by adopting the trained BP neural network. The method is beneficial to improving the accuracy and stability of foundation pit displacement prediction.

Description

technical field [0001] The invention belongs to the field of foundation pit displacement prediction, in particular to a foundation pit displacement prediction method based on particle swarm optimization BP neural network. Background technique [0002] With the rapid development of my country's modern urban infrastructure industry, the construction market continues to expand, and the construction of various office buildings, shopping malls, and schools has led to a continuous increase in the number of foundation pit projects, and the safety issues caused by foundation pit projects have also attracted people's attention. The problem of foundation pit monitoring has become one of the current research hotspots. With the continuous improvement of informatization and monitoring methods in the foundation pit construction process in recent years, the BP neural network has good self-adaption and real-time learning capabilities, and it can solve some engineering applications. It has a...

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Problems solved by technology

[0003] Liu Yonghui, Huang Li, Liu Bingkai, and Yuan Chang proposed in "Application and Accuracy Analysis of BP Neural Network in Deep Foundation Pit Monitoring and Forecasting" that the prediction method based on BP neural network is feasible and effective for the prediction of deep foundation pit deformation , but the initial weight of the BP neural network is relatively random, so it often occurs that it cannot converge at a high speed and the progress of the result is low during the training process.

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