Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 130-142 |
| Number of pages | 13 |
| Journal | Geotechnical Research |
| Volume | 5 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 13 2018 |
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ASJC Scopus subject areas
- Geotechnical Engineering and Engineering Geology
Cite this
A Bayesian definition of 'most probable' parameters. / Gardoni, Paolo; Biscontin, Giovanna.
In: Geotechnical Research, Vol. 5, No. 3, 13.09.2018, p. 130-142.Research output: Contribution to journal › Review article
}
TY - JOUR
T1 - A Bayesian definition of 'most probable' parameters
AU - Gardoni, Paolo
AU - Biscontin, Giovanna
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.
UR - http://www.scopus.com/inward/record.url?scp=85053635374&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053635374&partnerID=8YFLogxK
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 -