Affiliations: [a] School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu, China | [b] Suzhou Industrial Park Baiming Construction Co., Ltd. Suzhou, Jiangsu, China
Correspondence:
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Corresponding author: Hongjie Wu, School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, Jiangsu, China. E-mail: [email protected].
Abstract: Predicting the deformation of the foundation pit is one of the key issues for the construction safety of the foundation pit. In the traditional construction process often neglects the deformation prediction. It will cause the best time of repairing the pit is often missed. BP neural network has the characteristic of markova chain which is exactly match temporal-series data collected from displacement monitoring. So the BP neural network can understand the data better than SVM and RF. Further, the GA-BP neural network improved the training process based on BP neural network. We proposed a GA-BP neural network to predict the deformation of the foundation pit. To enforce the validation of the performance, we collected the real data from the Zhoushan foundation pit project. Compared with support vector regression and random forest regression, the results showed that GA-BP method has the error basically within -0.05–0.05 mm, the maximum relative error 0.36% and the predicted fitting value IA above 0.9, which are obviously better than other two methods.
Keywords: Foundation pit monitoring, genetic algorithm, BP neural network, time series and spatial features
