Journal of Jilin University(Earth Science Edition) ›› 2021, Vol. 51 ›› Issue (5): 1316-1323.doi: 10.13278/j.cnki.jjuese.20200310

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Prediction of Ground Settlement Around Deep Foundation Pit Based on Stacking Model Fusion

Qin Shengwu, Zhang Yanqing, Zhang Lingshuai, Miao Qiang, Cheng Qiushi, Su Gang, Sun Jingbo   

  1. College of Construction Engineering, Jilin University, Changchun 130026, China
  • Received:2020-12-17 Online:2021-09-26 Published:2021-09-29
  • Supported by:
    Supported by the National Natural Science Foundation of China (41977221) and Jilin Provincial Science and Technology Development Project (20190303103SF)

Abstract: In order to improve the prediction ability of machine learning in ground settlement of deep foundation pit, in this study,the authors proposed a ground settlement prediction method based on multi-model combination under Stacking framework. Taking a deep foundation pit in Shenzhen as an example, the Spearman correlation coefficient was used to screen the influencing factors of foundation pit ground settlement,and the eight influencing factors were used to establish the prediction model of ground settlement of deep foundation pit, so as to verify the applicability of this method. The mean absolute error, mean absolute error percentage, and root mean square error of the Stacking prediction model are 0.34, 2.22%, and 0.13, respectively. Compared with conventional base models (random forest, support vector machines, and artificial neural networks),the mean absolute error, mean absolute error percentage and root mean square error values of the Stacking prediction model are minimum.

Key words: foundation pit construction, surface subsidence, Stacking model fusion, impact factor screening

CLC Number: 

  • TU47
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