Journal of Applied Mathematics
Volume 2012 (2012), Article ID 487246, 20 pages
Research Article

A Mixed Prediction Model of Ground Subsidence for Civil Infrastructures on Soft Ground

1Graduate School of Management, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan
2Department of Civil Engineering, Osaka University, 2-1 Yamada-oka, Suita, Osaka 565-0871, Japan

Received 2 April 2012; Accepted 23 June 2012

Academic Editor: Qiang Ling

Copyright © 2012 Kiyoshi Kobayashi and Kiyoyuki Kaito. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The estimation of ground subsidence processes is an important subject for the asset management of civil infrastructures on soft ground, such as airport facilities. In the planning and design stage, there exist many uncertainties in geotechnical conditions, and it is impossible to estimate the ground subsidence process by deterministic methods. In this paper, the sets of sample paths designating ground subsidence processes are generated by use of a one-dimensional consolidation model incorporating inhomogeneous ground subsidence. Given the sample paths, the mixed subsidence model is presented to describe the probabilistic structure behind the sample paths. The mixed model can be updated by the Bayesian methods based upon the newly obtained monitoring data. Concretely speaking, in order to estimate the updating models, Markov Chain Monte Calro method, which is the frontier technique in Bayesian statistics, is applied. Through a case study, this paper discussed the applicability of the proposed method and illustrated its possible application and future works.