期刊
GEOTECHNICAL RESEARCH
卷 5, 期 3, 页码 130-142出版社
ICE PUBLISHING
DOI: 10.1680/jgere.18.00027
关键词
excavation; geotechnical engineering; statistical analysis
资金
- UK Engineering and Physical Sciences Research Council [EP/N021614/1]
- Technology Strategy Board for the University of Cambridge Centre for Smart Infrastructure and Construction [920035]
- Centre for Digital Built Britain, under Innovate UK [90066]
- China Scholarship Council
- EPSRC [EP/N021614/1] Funding Source: UKRI
Since guidelines for choosing 'most probable' parameters in ground engineering design codes are vague, concerns are raised regarding their definition, as well as the associated uncertainties. This paper introduces Bayesian inference for a new rigorous approach to obtaining the estimates of the most probable parameters based on observations collected during construction. Following the review of optimisation-based methods that can be used in back-analysis, such as gradient descent and neural networks, a probabilistic model is developed using Clough and O'Rourke's method for retaining wall design. Sequential Bayesian inference is applied to a staged excavation project to examine the applicability of the proposed approach and illustrate the process of back-analysis.
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