A Simplified Method of Incorporating Monitored Data for Settlement Prediction Using Bayesian Back Analysis Compared with Settle3
- DOI
- 10.2991/978-94-6463-258-3_56How to use a DOI?
- Keywords
- Bayesian updating; consolidation; embankment; settlement prediction
- Abstract
Simplification of the geotechnical model and soil parameters is common in engineering practice however review of the performance to verify and updated the prediction is seldom. However, oversimplification may not capture the appropriate conditions for reliable settlement prediction. Bayesian back analysis provides a way to update the adopted prior parameters using monitored data. Parameters such as the compression ratio, recompression ratio, creep strain rate and the coefficient of compressibility were treated as random variables. Prior predictions for a three layered model were analysed using two numerical analysis programs for comparison. Posterior predictions using a simplified model showed the surface settlement was well predicted utilising about 117 to 215 days of observed data. The settlement data was used to update the selected parameters through Bayesian back analysis to fit the time-settlement history.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Merrick Jones AU - Shan Huang AU - Jinsong Huang PY - 2023 DA - 2023/11/08 TI - A Simplified Method of Incorporating Monitored Data for Settlement Prediction Using Bayesian Back Analysis Compared with Settle3 BT - Proceedings of the Rocscience International Conference (RIC 2023) PB - Atlantis Press SP - 605 EP - 614 SN - 2589-4943 UR - https://doi.org/10.2991/978-94-6463-258-3_56 DO - 10.2991/978-94-6463-258-3_56 ID - Jones2023 ER -