Table 1 The data driven methods for ground settlement prediction in the previous studies.
From: Ground settlement prediction for highway subgrades with sparse data using regression Kriging
References | Dataset type | Techniques |
|---|---|---|
Al-Shamrani (2005)30 | Organic—rich soil settlement | Hyperbolic method |
Suwansawat and Einstein (2006)31 | Tunneling— induced ground settlement | Neural network model |
Fan et al. (2013)32 | Tunneling—induced ground settlement | Wavelet smooth relevance vector machine |
Kanayama et al. (2014)10 | Soil settlement | Neural network model |
Park et al. (2015)27 | Thick soft clay settlement | Genetic algorithm |
Moghaddasi and Noorian-Bidgoli (2018)33 | Tunneling—induced ground settlement | Multiple regression methods |
Tao et al. (2020)11 | Soil settlement | Ensemble Kalman filtering method |
Chen et al. (2021)34 | Dam settlement | Clustering - based ensemble learning method |
Wan and Doherty (2022)29 | Embankment settlement | Data-driven multi-stage method |
Tian and Wang (2023)28 | Soil settlement | Physics-informed Bayesian learning method |
Zhou et al. (2023)15 | Tunneling—induced ground settlement | XGBoost algorithm |
