TY - JOUR
T1 - A Bayesian definition of 'most probable' parameters
AU - Gardoni, Paolo
AU - Biscontin, Giovanna
N1 - Funding Information:
This work was supported by the UK Engineering and Physical Sciences Research Council grant EP/N021614/1, the Technology Strategy Board grant 920035 for the University of Cambridge Centre for Smart Infrastructure and Construction and a miniprojects award from the Centre for Digital Built Britain, under Innovate UK grant number 90066. Yingyan Jin was supported by the China Scholarship Council. Additional resources were provided by the Centre for Digital Built Britain.
Publisher Copyright:
© 2018 Published with permission by the ICE under the CC-BY 4.0 license.
PY - 2018/9/13
Y1 - 2018/9/13
N2 - 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.
AB - 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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U2 - 10.1680/jgere.18.00027
DO - 10.1680/jgere.18.00027
M3 - Review article
AN - SCOPUS:85053635374
VL - 5
SP - 130
EP - 142
JO - Geotechnical Research
JF - Geotechnical Research
SN - 2052-6156
IS - 3
ER -