- Title
- A generic framework for geotechnical subsurface modeling with machine learning
- Creator
- Xie, Jiawei; Huang, Jinsong; Zeng, Cheng; Huang, Shan; Burton, Glen J.
- Relation
- ARC.DP190101592 http://purl.org/au-research/grants/arc/DP190101592
- Relation
- Journal of Rock Mechanics and Geotechnical Engineering Vol. 14, Issue 5, p. 1366-1379
- Publisher Link
- http://dx.doi.org/10.1016/j.jrmge.2022.08.001
- Publisher
- Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
- Resource Type
- journal article
- Date
- 2022
- Description
- This study introduces a generic framework for geotechnical subsurface modeling, which accounts for spatial autocorrelation with local mapping machine learning (ML) methods. Instead of using XY coordinate fields directly as model input, a series of autocorrelated geotechnical distance fields (GDFs) is designed to enable the ML models to infer the spatial relationship between the sampled locations and unknown locations. The whole framework using GDF with ML methods is named GDF-ML. This framework is purely data-driven which avoids the tedious work in the scale of fluctuations (SOFs) estimating and data detrending in the conventional spatial interpolation methods. Six local mapping ML methods (extra trees (ETs), gradient boosting (GB), extreme gradient boosting (XGBoost), random forest (RF), general regression neural network (GRNN) and k-nearest neighbors (KNN)) are compared in the GDF-ML framework. The results show that the GDFs are better than the conventional XY coordinate fields based ML methods in both accuracy and spatial continuity. GDF-ML is flexible which can be applied to high-dimensional, multi-variable and incomplete datasets. Among these six methods, GDF with ET method (GDF-ET) clearly shows the best accuracy and best spatial continuity. The proposed GDF-ET method can provide a fast and accurate interpretation of the soil property profile. Sensitivity analysis shows that this method is applicable to very small training dataset size. The associated statistical uncertainty can also be quantified so that the reliability of the subsurface modeling results can be estimated objectively and explicitly. The uncertainty results clearly show that the prediction becomes more accurate when more sampled data are available.
- Subject
- site investigation; machine learning (ML); spatial interpolation; geotechnical distance fields (GDFs); tree-based models
- Identifier
- http://hdl.handle.net/1959.13/1474510
- Identifier
- uon:49300
- Identifier
- ISSN:1674-7755
- Language
- eng
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