Research on Geotechnical Data Interpolation and Prediction Techniques

DOI

10.2991/978-94-6463-256-9_182How to use a DOI?

Keywords

underground space; geological exploration; missing data; geotechnical data interpolation; machine learning; regression models

Abstract

The development of underground space is vital for urbanization and infrastructure projects. Prior to construction, comprehensive geological exploration is essential to ensure stability and safety. However, acquiring complete and accurate statistical data for project management is challenging, necessitating the handling of missing data to enhance reliability. Interpolation techniques are an effective way of dealing with incomplete data. This study presents a scalable framework for geotechnical data interpolation using machine learning. The framework employs different regression models to construct estimators and accurately interpolate geotechnical data. Key considerations include model selection and parameter optimization, with complete data used as the regression target. Five regression models, Bayesian Ridge Regression (BR), Extreme Gradient Boosting Tree (XGBoost), Support Vector Machine (SVR), Random Forest (RF) and K-Nearest Neighbour (KNN), were utilised. Estimators are constructed using the regression models and iterative interpolation is used to estimate missing values for geotechnical data, with each feature treated as a result of using the different estimators. The framework is evaluated through k-fold cross-validation, demonstrating its effectiveness in imputing missing values. The interpolation results using the SVR model indicate good conformity with the original data, confirming the method's effectiveness in capturing underlying patterns. This scalable framework bridges the gap in geotechnical data interpolation research, providing a reliable solution. The proposed approach contributes to the accurate and robust interpolation of geotechnical data, facilitating informed decision-making in underground construction projects.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Haiyong Liu
AU  - Yangyang Chen
AU  - Lu Zhao
AU  - Wen Liu
PY  - 2023
DA  - 2023/10/09
TI  - Research on Geotechnical Data Interpolation and Prediction Techniques
BT  - Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023)
PB  - Atlantis Press
SP  - 1788
EP  - 1795
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-256-9_182
DO  - 10.2991/978-94-6463-256-9_182
ID  - Liu2023
ER  -