TY - JOUR
T1 - Comparative spatial predictions of the locations of soil-rock interface
AU - Qi, Xiaohui
AU - Pan, Xiaohua
AU - Chiam, Kiefer
AU - Lim, Yong Siang
AU - Lau, Sze Ghiong
N1 - Funding Information: This research is supported by the Singapore Ministry of National Development and the National Research Foundation, Prime Minister's Office under the Land and Liveability National Innovation Challenge (L2 NIC) Research Programme (Award No. L2NICCFP2-2015-1). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the Singapore Ministry of National Development and National Research Foundation, Prime Minister's Office, Singapore. The authors are grateful to Prof. Chu Jian for his helpful comments and suggestions.
PY - 2020/7/1
Y1 - 2020/7/1
N2 - 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
AB - 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
KW - Conditional random field method
KW - Polynomial regression
KW - Soil-rock interface
KW - Spatial prediction
KW - Spline interpolation
KW - Spline regression
UR - http://www.scopus.com/inward/record.url?scp=85082862738&partnerID=8YFLogxK
U2 - 10.1016/j.enggeo.2020.105651
DO - 10.1016/j.enggeo.2020.105651
M3 - Article
AN - SCOPUS:85082862738
SN - 0013-7952
VL - 272
JO - Engineering Geology
JF - Engineering Geology
M1 - 105651
ER -