Abstract
The prediction of foundation pit deformation is of great significance in ensuring the safety of neighbors and efficiency of construction. The back-propagation neural network (BPNN) is one of the most successful models that have been implemented for data prediction. An accurate mapping relationship cannot be constructed by directly applying neural networks due to the measured data of foundation pit suffer from small quantity and big noise. In the present study, the main idea of residual network (ResNet) was introduced in prediction, and genetic algorithm (GA) and back propagation (BP) were coupled to develop a hybrid GA-ResNN training algorithm with global search capabilities. The dimension of the input data is doubled by overlapping and encoding the input data. The dependence of the neural network on the initial model can be reduced using GA and ResNet. Cases reported in the literature are used to demonstrate the effectiveness and accuracy of the proposed model.
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Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
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Funding
This work was supported by the Young Experts of Taishan Scholar Project of Shandong Province (No. tsqn202103163), the National Natural Science Foundation of China (No. 52078278, No. 51778345), the Key Research and Development Foundation of Shandong Province of China (No. 2019GSF109006), and the program of Qilu Young Scholars of Shandong University. Great appreciation goes to the editorial board and the reviewers of this paper.
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All authors participated in drafting the article and agreed to submit it for publication. Wei Cui was project supervisor and was responsible for study conception. Chunyu Cui was responsible for data analysis, the revision, and writing the manuscript. Shanwei Liu and Bin Ma were responsible for the proofreading and reviewing the manuscript.
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Responsible Editor: Zeynal Abiddin Erguler
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Cui, Cy., Cui, W., Liu, Sw. et al. An optimized neural network with a hybrid GA-ResNN training algorithm: applications in foundation pit. Arab J Geosci 14, 2443 (2021). https://doi.org/10.1007/s12517-021-08775-9
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DOI: https://doi.org/10.1007/s12517-021-08775-9