Spatial-temporal prediction of secondary compression using random field theory

被引:5
作者
Rungbanaphan, Pongwit [1 ]
Honjo, Yusuke [2 ]
Yoshida, Ikumasa [3 ]
机构
[1] Shimizu Corp, Civil Engn Technol Div, Design Dept, Minato Ku, Tokyo 1058007, Japan
[2] Gifu Univ, Gifu, Japan
[3] Tokyo City Univ, Tokyo, Japan
关键词
Secondary compression; Settlement prediction; Statistical analysis; Spatial correlation; Random field; Bayesian estimation; SIMULATION; SOIL; CONSOLIDATION;
D O I
10.1016/j.sandf.2012.01.013
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
A methodology is presented for observation-based settlement predictions by considering the spatial correlation structure of soil. The spatial correlation is introduced among the settlement model parameters, and the settlements at various points are spatially correlated through these geotechnical parameters, which naturally describe the phenomenon. The method is based on Bayesian estimations, considering both prior information, including spatial correlation, and observed settlements, to search for the best estimates of the parameters. Within the Bayesian framework, the optimized selection of the auto-correlation distance, by Akaike's Bayesian Information Criterion (ABIC), and the spatial interpolation of the model parameters, by the kriging method, are also proposed. The application of the proposed approach in secondary compression settlement predictions, based on the linear relationship between settlement and the logarithm of time, is presented in this paper. Several case studies are carried out using both simulated settlement data and actual field observation data. It is concluded that the accuracy of settlement predictions can be improved by taking into account the spatial correlation structure, especially when the spacing of the observation points is shorter than half of the auto-correlation distance, and that the proposed approach produces rational predictions of settlements at any location and at any time with quantified errors. (C) 2012. The Japanese Geotechnical Society. Production and hosting by Elsevier B.V. All rights reserved.
引用
收藏
页码:99 / 113
页数:15
相关论文
共 50 条
[41]   Evaluation of Shear Strength of RC Beams Without Shear Reinforcement Using Modified Compression Field Theory [J].
Mohammad Sattari ;
Mahmoud R. Banan ;
Mohammad R. Banan .
Iranian Journal of Science and Technology, Transactions of Civil Engineering, 2019, 43 :463-477
[42]   Evaluation of Shear Strength of RC Beams Without Shear Reinforcement Using Modified Compression Field Theory [J].
Sattari, Mohammad ;
Banan, Mahmoud R. ;
Banan, Mohammad R. .
IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING, 2019, 43 (03) :463-477
[43]   Stochastic Analysis of Hydraulic Fracture Propagation using the eXtended Finite Element Method and Random Field Theory [J].
Youn, D-J. ;
Griffiths, D. V. .
INTEGRATING INNOVATIONS OF ROCK MECHANICS, 2015, :189-196
[44]   Reliability analysis of unsaturated soil slope stability using spatial random field-based Bayesian method [J].
Huang, M. L. ;
Sun, D. A. ;
Wang, C. H. ;
Keleta, Y. .
LANDSLIDES, 2021, 18 (03) :1177-1189
[45]   Pore-Scale Prediction of the oxygen effective diffusivity in porous battery electrodes using the random walk theory [J].
Wang, Fangzhou ;
Li, Xianglin ;
Tan, Jianyu ;
Hao, Xiaowen ;
Xiong, Bo .
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, 2022, 183
[46]   High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and Gaussian Markov random fields [J].
Simpson, Emma S. ;
Opitz, Thomas ;
Wadsworth, Jennifer L. .
EXTREMES, 2023, 26 (04) :669-713
[47]   Spreading Sea Clutter Suppression for High-Frequency Hybrid Sky-Surface Wave Radar Using Orthogonal Projection in Spatial-Temporal Domain [J].
Zhou, Qing ;
Bai, Yufan ;
Zhu, Xiaohua ;
Wu, Xiongbin ;
Hong, Hong ;
Ding, Chuanwei ;
Zhao, Heng .
REMOTE SENSING, 2024, 16 (13)
[48]   A framework for continuous fingerspelling spotting for H.264/AVC compressed videos using spatio-temporal Markov random field [J].
Talukdar, Anjan Kumar ;
Bhuyan, M. K. .
MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (18) :28329-28347
[49]   Uncertainty with Varying Subsurface Permeabilities Reduced Using Coupled Random Field and Extended Theory of Porous Media Contaminant Transport Models [J].
Seyedpour, S. M. ;
Henning, C. ;
Kirmizakis, P. ;
Herbrandt, S. ;
Ickstadt, K. ;
Doherty, R. ;
Ricken, T. .
WATER, 2023, 15 (01)
[50]   Precise estimation of pressure–temperature paths from zoned minerals using Markov random field modeling: theory and synthetic inversion [J].
Tatsu Kuwatani ;
Kenji Nagata ;
Masato Okada ;
Mitsuhiro Toriumi .
Contributions to Mineralogy and Petrology, 2012, 163 :547-562