Abstract
The purpose of this study was to predict the deformation of a deep foundation pit based on a combination model of wavelet transform and gray BP neural network. Using a case of a deep foundation pit, a combination model of wavelet transform and gray BP neural network was used to predict the deformation of the deep foundation pit. The results show that compared with the traditional gray BP neural network model, the relative error of the combination model of wavelet transform and gray BP neural network was reduced by 2.38%. This verified that the combined model has high accuracy and reliability in the prediction of foundation pit deformation and also conforms to the actual situation of the project. The research results can provide a valuable reference for foundation pit deformation monitoring.
Highlights
At present, with the vigorous development of large-scale infrastructure in China, the excavation process of foundation pits will cause the deformation of foundation pits themselves, and cause the deformation and displacement of adjacent buildings, rail transit, and underground comprehensive pipe corridors
The above methods have achieved certain results in the deformation prediction of foundation pit, but there are shortcomings. e convergence speed of artificial neural network is relatively slow, which makes local optimization easy, making the prediction result error larger; the gray correlation degree theory can reduce the prediction accuracy of the model when dealing with the situation of large data fluctuation; the fuzzy comprehensive evaluation model still has great difficulty in choosing which membership function. erefore, many scholars try to use the hybrid combination prediction model to predict the deformation of foundation pits
The effective and reliable data of foundation pit deformation were obtained by wavelet denoising; secondly, the GM (1, 1) model was used to predict foundation pit deformation, and the predicted value was taken as the input sample value of BP neural network; and the expected output prediction result of foundation pit deformation was obtained by learning and training. e contribution of this study is twofold: (1) e innovation of this study is to propose a new gray BP neural network method based on wavelet denoising
Summary
With the vigorous development of large-scale infrastructure in China, the excavation process of foundation pits will cause the deformation of foundation pits themselves, and cause the deformation and displacement of adjacent buildings, rail transit, and underground comprehensive pipe corridors. Erefore, these mixed combination model methods do not fully consider or distinguish the characteristics and influence of the trend term and the random term in the prediction process, and they have many shortcomings, such as being relatively simple and having their own characteristics and application occasions, so they cannot fully mine the original data information, and the prediction accuracy needs to be improved [16]. Compared with the traditional gray BP neural network model, this method can improve the accuracy and stability of deformation prediction of deep foundation pit, more accurately predict the future deformation of deep foundation pit, and be more in line with the engineering practice.
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