- Title
- An approach for predicting embankment settlement by integrating multi-source information
- Creator
- Zheng, Dong; Huang, Jin-song; Li, Dian-qing
- Relation
- Yantu Lixue Vol. 40, Issue 2, p. 709-727
- Publisher Link
- http://dx.doi.org/10.16285/j.rsm.2017.1400
- Publisher
- Zhongguo Kexueyuan Wuhan Yantu Lixue Yanjiusuo
- Resource Type
- journal article
- Date
- 2019
- Description
- Accurate prediction of embankment settlement is critical for risk mitigation and cost reduction in embankment projects. Traditionally, the prediction only using data from site investigation usually deviates from the monitored settlement. In this article, it is proposed to integrate multi-source information based on Bayesian theory to predict embankment settlement. The finite element method is adopted to simulate the consolidation process of multiple soil layers, and the posterior high-dimensional distributions of soil parameters are obtained by efficient Markov Chain Monte Carlo simulation. The proposed method is validated by site data from a trial embankment constructed at Ballina, New South Wales, Australia. The results indicate that the proposed multi-sources information integration method based on Bayesian theory can effectively integrate date from site investigation and field monitoring, based on which the embankment settlement can be accurately predicted. For the trial embankment at Ballina, the accuracy of prediction is improved in terms of the overall trend as more monitored data is incorporated into Bayesian updating. The accuracy of surface prediction can be satisfied based on data from 0-116 d, while the data of 0-496 d can be used to monitor settlement for all the monitoring points. For the Ballina embankment, the prior information affects slightly on the posterior prediction, while the measurement error barely affects the prediction.
- Subject
- embankment; information fusion; consolidation; prediction; Bayesian theory
- Identifier
- http://hdl.handle.net/1959.13/1415166
- Identifier
- uon:36866
- Identifier
- ISSN:1000-7598
- Language
- CA
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