• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 57 Issue 6
Dec.  2022
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Article Contents
YU Yongtang, ZHENG Jianguo. New Prediction Model for Post-Construction Settlement of Loess High Fill Site[J]. Journal of Southwest Jiaotong University, 2022, 57(6): 1268-1276, 1292. doi: 10.3969/j.issn.0258-2724.20200872
Citation: YU Yongtang, ZHENG Jianguo. New Prediction Model for Post-Construction Settlement of Loess High Fill Site[J]. Journal of Southwest Jiaotong University, 2022, 57(6): 1268-1276, 1292. doi: 10.3969/j.issn.0258-2724.20200872

New Prediction Model for Post-Construction Settlement of Loess High Fill Site

doi: 10.3969/j.issn.0258-2724.20200872
  • Received Date: 12 Jan 2021
  • Rev Recd Date: 01 Apr 2021
  • Available Online: 09 Oct 2022
  • Publish Date: 26 May 2021
  • The post-construction settlement prediction of loess high fill site is an important basis for determining the time sequence of construction and the spatial layout of ground engineering. In order to accurately predict the post-construction settlement of loess high fill site, two post-construction settlement predictive models, one is a convergent model and the other is a divergent one, were proposed based on the characteristics of settlement data and the evolution laws of settlement curves of two typical loess high fill sites. The basic properties of the models, the solution to the parameters of the models, and the applicability of the proposed models in predicting the post-construction settlement of typical loess high fill site were all presented in detail.The results show that the proposed models are suitable for post-construction settlement prediction of loess high fill site due to its smaller fitting error and prediction error. Moreover, it is found that the divergent model is more suitable to the grounds whose settlement curves have S-shape, the mean absolute percentage error (MAPE) is 4.6%, which is 78.7%−95.8% lower than the prediction error of the traditional prediction models, while the convergence model is more applicable to the grounds whose settlement curves have J-shape, the mean absolute percentage error (MAPE) is 1.9%, which is 68.3%−84.4% lower than the prediction error of the traditional prediction models. Due to their good adaptability, generality and stability, the proposed models can provide more choices and references for the prediction and evaluation of post-construction settlement of loess high fill site in the future.

     

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