4.6 Article

Machine learning of three-dimensional subsurface geological model for a reclamation site in Hong Kong

Related references

Note: Only part of the references are listed.
Article Engineering, Geological

Data-driven sequential development of geological cross-sections along tunnel trajectory

Chao Shi et al.

Summary: A data-driven framework is proposed to predict geological cross-sections ahead of tunnel face. This method incorporates geological information revealed from tunnel excavation as additional data, providing accurate prediction of geological cross-sections along planned tunnel trajectory with quantified stratigraphic uncertainty.

ACTA GEOTECHNICA (2023)

Article Geosciences, Multidisciplinary

Similarity quantification of soil parametric data and sites using confidence ellipses

Liang Han et al.

Summary: This paper presents a confidence ellipse-based method to evaluate the similarity of soil parametric data, and further estimates the site similarity through two proposed strategies. The results show the effectiveness of this method in evaluating parametric data similarity and reducing transformation uncertainty.

GEOSCIENCE FRONTIERS (2022)

Article Engineering, Geological

Rockhead profile simulation using an improved generation method of conditional random field

Liang Han et al.

Summary: In this study, the conditional random field (CRF) was improved to simulate rockhead profiles. With the assistance of Bayesian theory, the proposed method utilizes measurement data and prior information to handle uncertainty. The method provides reasonable estimations of rockhead depth at various locations and reduces subjectivity in determining prior mean.

JOURNAL OF ROCK MECHANICS AND GEOTECHNICAL ENGINEERING (2022)

Article Construction & Building Technology

Two-dimensional prediction of the interface of geological formations: A comparative study

Xiaohui Qi et al.

Summary: This paper evaluates three commonly used spatial prediction methods for geological interfaces and proposes a zonation method to improve prediction accuracy. The results show that the multivariate adaptive spline regression (MARS) method can more clearly depict the spatial trend of geological interfaces.

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2022)

Article Construction & Building Technology

Data-driven construction of Three-dimensional subsurface geological models from limited Site-specific boreholes and prior geological knowledge for underground digital twin

Chao Shi et al.

Summary: In this study, a data-driven and deep learning method called IC-XGBoost3D is proposed for building a 3D geological model from limited site-specific boreholes and 2D training images reflecting prior geological knowledge. The method efficiently generates an anisotropic 3D geological model with high prediction accuracy and provides quantitative evaluation of associated stratigraphic uncertainty. Effects of irregular borehole spacing and single training image on simulation performance are also investigated.

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2022)

Article Engineering, Geological

Challenges in data-driven site characterization

Kok-Kwang Phoon et al.

Summary: Data-driven site characterisation, relying solely on measured data for site description, is crucial for industries undergoing rapid digital transformation. Challenges include data quality, accuracy, and practicality in application.

GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS (2022)

Article Engineering, Geological

Nonparametric and data-driven interpolation of subsurface soil stratigraphy from limited data using multiple point statistics

Chao Shi et al.

Summary: A novel nonparametric, data-driven method based on multiple point statistics (MPS) is proposed to interpolate subsurface soil stratigraphy from sparse measurements. The method, which utilizes Bayesian supervised machine learning, is the first purely data-driven method for geotechnical site characterization. The effectiveness of the method is demonstrated through a simulated example and real data from a reclamation site in Hong Kong, providing accurate interpolation and quantification of uncertainty.

CANADIAN GEOTECHNICAL JOURNAL (2021)

Article Computer Science, Artificial Intelligence

Machine learning method for CPTu based 3D stratification of New Zealand geotechnical database sites

Shengchao Wu et al.

Summary: The study proposes a method for 3D geotechnical site stratification based on CPTu measurements, which achieves 3D interpolation from 1D soil stratification. The method includes a soil classification model, soil layer boundary identification method, and incorporates local variation information to significantly improve identification accuracy.

ADVANCED ENGINEERING INFORMATICS (2021)

Article Engineering, Geological

Smart determination of borehole number and locations for stability analysis of multi-layered slopes using multiple point statistics and information entropy

Chao Shi et al.

Summary: This study introduces a smart sampling strategy based on multiple point statistics and information entropy for interpreting subsurface stratigraphy of multi-layered slopes and adaptively determining optimal borehole locations and numbers. The data-driven approach effectively reduces stratigraphic uncertainty.

CANADIAN GEOTECHNICAL JOURNAL (2021)

Article Engineering, Geological

Training image selection for development of subsurface geological cross-section by conditional simulations

Chao Shi et al.

Summary: A data-driven method based on edge orientation detection is proposed for selecting the optimal training image for delineation of subsurface geological cross-section. This method successfully differentiates soil stratigraphic patterns between different training images, providing a quantitative indicator for selection of the optimal training image.

ENGINEERING GEOLOGY (2021)

Article Engineering, Geological

Development of Subsurface Geological Cross-Section from Limited Site-Specific Boreholes and Prior Geological Knowledge Using Iterative Convolution XGBoost

Chao Shi et al.

Summary: The delineation of vertical geological cross-sections is crucial in geotechnical site characterization, especially in complex geological settings with limited boreholes. A novel iterative convolution eXtreme Gradient Boosting model is proposed in this study, which interpolates a subsurface geological cross-section from limited site-specific boreholes and a training geological cross-section from previous projects. This data-driven method does not require specifying parametric function form and can learn stratigraphic patterns automatically.

JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING (2021)

Article Engineering, Civil

Prediction of interfaces of geological formations using the multivariate adaptive regression spline method

Xiaohui Qi et al.

Summary: The study used the MARS method to predict the elevations of geological interfaces, showing good accuracy compared to borehole data. Furthermore, the MARS method was able to generate reasonable prediction intervals that properly reflected the data density and geological complexity.

UNDERGROUND SPACE (2021)

Article Political Science

Housing Policies in Hong Kong

Tai Wei Lim

EAST ASIAN POLICY (2020)

Article Engineering, Geological

Probabilistic analysis and design of stabilizing piles in slope considering stratigraphic uncertainty

Wenping Gong et al.

ENGINEERING GEOLOGY (2019)

Article Chemistry, Multidisciplinary

A Stratigraphic Prediction Method Based on Machine Learning

Cuiying Zhou et al.

APPLIED SCIENCES-BASEL (2019)

Article Computer Science, Interdisciplinary Applications

A training image evaluation and selection method based on minimum data event distance for multiple-point geostatistics

Wenjie Feng et al.

COMPUTERS & GEOSCIENCES (2017)

Article Computer Science, Interdisciplinary Applications

Verifying the high-order consistency of training images with data for multiple-point geostatistics

Cristian Perez et al.

COMPUTERS & GEOSCIENCES (2014)