Probabilistic Evaluation of a Seismic Site Class from Electrical Resistivity Test Data

Kuili, Saikat; Jakka, Ravi S.

doi:10.1007/s00024-023-03364-1

Pure Appl. Geophys.

2023

DOI: 10.1007/s00024-023-03364-1

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Probabilistic Evaluation of a Seismic Site Class from Electrical Resistivity Test Data

Saikat Kuili,

Ravi S. Jakka

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2024

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Cited by 1 publication

References 58 publications

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

Huang,

Qin,

Dai

et al. 2024

Sci Rep

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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 error (RMSE), mean absolute error (MAE), mean arctangent absolute percent error (MAAPE) and scatter index (SCI)) are significantly decreased when the Box–Cox transformation is incorporated. In addition, the first-order polynomial trend structure is found to be more appropriate than those with higher orders for predicting settlements resulting from primary consolidation. Moreover, comparative study is conducted among the proposed RK method, classical prediction methods and back propagation neural network (BPNN). It is found that the evaluation metrics obtained by the RK method are significantly smaller than those obtained by the other methods, indicating its highest accuracy. By contrast, BPNN has the worst performance among the various methods, because the sparse data are inadequate to establish a satisfactory BPNN model. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-75811-5.

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

Huang,

Qin,

Dai

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Probabilistic Evaluation of a Seismic Site Class from Electrical Resistivity Test Data

Cited by 1 publication

References 58 publications

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

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

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