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

ABSTRACT: Ground settlement prediction for highway subgrades is crucial in related engineering projects. When predicting the ground settlement, sparse sample data are often encountered in practice, which greatly affects the prediction accuracy. However, this has been seldom explored in previous studies. To resolve it, this paper proposes a regression Kriging (RK)-based method for ground settlement prediction with sparse data. Under the framework of RK, the stationarity of sample residual and trend structure are key factors for prediction accuracy. It is found that the use of Box-Cox transformation, which can help to achieve stationarity of sample residual, leads to significant increase of the prediction accuracy with sparse data. Specifically, the various evaluation metrics (i.e., root mean square e

SUBMITTER: Huang L 

PROVIDER: S-EPMC11490503 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature