Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154276
Title: Comparative spatial predictions of the locations of soil-rock interface
Authors: Qi, Xiaohui
Pan, Xiaohua
Chiam, Kiefer
Lim, Yong Siang
Lau, Sze Ghiong
Keywords: Engineering::Civil engineering
Issue Date: 2020
Source: Qi, X., Pan, X., Chiam, K., Lim, Y. S. & Lau, S. G. (2020). Comparative spatial predictions of the locations of soil-rock interface. Engineering Geology, 272, 105651-. https://dx.doi.org/10.1016/j.enggeo.2020.105651
Project: L2NICCFP2-2015-1)
Journal: Engineering Geology
Abstract: The location of soil-rock interfaces (SRIs) may significantly affect the underground construction works, including the design of underground geotechnical structures. The prediction of the location of SRI using limited borehole data is a challenging task. To address this challenge, this paper presents a comparison study of four methods for spatially predicting the SRI elevation, namely the polynomial regression, spline interpolation, one-dimensional spline regression, and a Bayesian-based conditional random field. The consistencies, prediction accuracies, patterns of the predicted curves, and prediction uncertainties for various methods are evaluated. Borehole data from two sites in Singapore are used in the comparative study. The results show that the spline interpolation method produces the least consistent estimation of SRI profiles. The spline interpolation method also has lower prediction accuracies than the other three methods and cannot provide any information regarding the prediction uncertainty. The spatial trend of the geological interface cannot be captured by the polynomial regression method with a relatively high (i.e., 10) order of the polynomial when faults and folds exist. Advantages of the spline regression method over the conditional random field methods include that (i) it provides a clear and explicit spatial trend of the SRI, which well reflects the geological complexity of the sites; (ii) it avoids the cumbersome estimation of random field parametric values, which is a challenging task under the condition of limited data; and (iii) it can differentiate the zones with different prediction accuracies, which cannot be accomplished by the conditional random field method due to limited data. To sum up, the spline regression method produces a simpler and more informative curve of the SRI than the other three methods and thereby is useful as it can guide site investigations to be carried out at geologically uncertain areas to reduce risks, especially for underground construction projects
URI: https://hdl.handle.net/10356/154276
ISSN: 0013-7952
DOI: 10.1016/j.enggeo.2020.105651
Rights: © 2020 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

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