Projects per year
Abstract
Conventional site investigation schemes typically use an empirical sampling strategy, which involves equal sampling spacing with regular grid patterns. However, this approach assigns equal importance to the entire site and does not account for subsurface stratigraphic variations and site constraints. In this study, a data-driven multi-stage sampling strategy is proposed to adaptively optimize the borehole locations for a three-dimensional (3D) geotechnical site, considering the subsurface stratigraphic uncertainties and irregular site geometries. The initial sampling plan is determined based on weighted centroidal Voronoi tessellation, which assigns various sampling densities to zones depending on their importance. Measurements obtained in the previous sampling stage are combined with prior geological knowledge to establish and update a 3D geological domain using a physics-informed geological modelling method. The next optimal location for sampling is adaptively determined with the objective of maximizing the reduction in the stratigraphic uncertainty. The proposed method represents the first data-driven 3D site planning method that explicitly considers 3D subsurface stratigraphic variations. The performance of the proposed multi-stage sampling strategy is illustrated using a simulation study. The results indicate that the proposed method efficiently identifies the optimal sampling locations while comprehensively considering the 3D subsurface geological uncertainties and irregular site geometries. © 2023 Elsevier B.V.
Original language | English |
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Article number | 107301 |
Journal | Engineering Geology |
Volume | 325 |
Online published | 9 Sept 2023 |
DOIs | |
Publication status | Published - Nov 2023 |
Research Keywords
- 3D stratigraphic uncertainty
- Information entropy
- Sampling optimization
- Site investigation
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Dive into the research topics of 'Data-driven multi-stage sampling strategy for a three-dimensional geological domain using weighted centroidal voronoi tessellation and IC-XGBoost3D'. Together they form a unique fingerprint.Projects
- 2 Finished
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ITF: Artificial Intelligence Based Technology For Characterization Of Subsurface Rocks Surrounding Tunnel Boring Machine (TBM) And TBM Automatic Control
HUANG, G. (Principal Investigator / Project Coordinator) & Li, X. (Co-Investigator)
1/12/22 → 31/01/25
Project: Research
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GRF: Multiscale Machine Learning of Subsurface Stratigraphy from Limited Site-specific Measurements and Prior Geological Knowledge using Iterative Convolutional Neural Networks (CNN)
WANG, Y. (Principal Investigator / Project Coordinator)
1/01/22 → 2/10/24
Project: Research