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- W4387659708 abstract "Geotechnical investigation is an important site characterization process in engineering construction. Excavation pits and boreholes are popular geotechnical investigation methods for accessing subsurface soil and rock characteristics. Although they are accurate, they are limited to the discrete locations where tests are carried out. The demand for 3D representation has been increasing as an alternative for improved visualization of geotechnical investigations. This study developed a procedure for 3D modelling of geotechnical investigations using open-source software. It improves 3D visualization of geotechnical investigations and characterization of accuracy and uncertainty assessment. Presently, most geotechnical investigations do not regularly report uncertainty assessment despite its importance in risk analysis of the investigations. The procedure developed in this study was tested in a proposed site for construction of an Inland Container Depot where it depicted geotechnical soil properties with more than 80% accuracy on holdout samples. It was able to model both horizontal and vertical variations of the soil properties with quantification of uncertainty. More testing and wide applications are recommended." @default.
- W4387659708 created "2023-10-16" @default.
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- W4387659708 date "2023-10-09" @default.
- W4387659708 modified "2023-10-17" @default.
- W4387659708 title "Application of Machine Learning and 3D Modelling to Improve Geotechnical Site Characterization" @default.
- W4387659708 doi "https://doi.org/10.20944/preprints202310.0495.v1" @default.
- W4387659708 hasPublicationYear "2023" @default.
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