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Ground settlement prediction for highway subgrades with sparse data using regression Kriging.

Lei Huang1, Wei Qin2,3, Guo-Liang Dai4

  • 1College of Architecture and Civil Engineering, Sanming University, Sanming, 365004, China.

Scientific Reports
|October 19, 2024
PubMed
Summary

Predicting highway subgrade settlement with sparse data is improved using a regression Kriging (RK) method. Incorporating Box-Cox transformation enhances accuracy, outperforming traditional methods and neural networks for reliable ground settlement prediction.

Keywords:
Box–Cox transformationGround settlement predictionRegression Kriging (RK)Sparse sample data

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Area of Science:

  • Geotechnical Engineering
  • Civil Engineering
  • Environmental Engineering

Background:

  • Accurate ground settlement prediction for highway subgrades is critical for infrastructure projects.
  • Sparse sample data frequently challenges prediction accuracy in real-world scenarios, a problem under-explored previously.
  • Existing methods struggle with data scarcity, necessitating novel approaches for reliable settlement forecasting.

Purpose of the Study:

  • To propose and evaluate a regression Kriging (RK)-based method for enhanced ground settlement prediction using sparse data.
  • To investigate the impact of Box-Cox transformation and trend structure on RK prediction accuracy.
  • To compare the performance of the proposed RK method against classical techniques and back propagation neural networks (BPNN).

Main Methods:

  • A regression Kriging (RK) framework was employed for ground settlement prediction.
  • Box-Cox transformation was utilized to achieve stationarity of sample residuals, improving prediction.
  • A first-order polynomial trend structure was identified as optimal for primary consolidation settlement prediction.

Main Results:

  • The integration of Box-Cox transformation significantly decreased evaluation metrics (RMSE, MAE, MAAPE, SCI), boosting prediction accuracy with sparse data.
  • The first-order polynomial trend structure proved more suitable than higher orders for primary consolidation settlement.
  • The proposed RK method demonstrated superior accuracy compared to classical methods and BPNN, which performed poorly due to data limitations.

Conclusions:

  • The regression Kriging (RK) method, enhanced with Box-Cox transformation and a first-order polynomial trend, offers a highly accurate solution for ground settlement prediction with sparse data.
  • This approach significantly improves upon traditional methods and back propagation neural networks (BPNN) in scenarios with limited data.
  • The findings highlight the importance of data transformation and appropriate trend modeling for robust geotechnical predictions.