Bayesian inference for deep excavations
W.J. de Wolf (Fugro , TU Delft - Geo-engineering)
M. Korff (TU Delft - Geo-engineering, Deltares)
A. van Seters (Fugro )
J.H. van Dalen (TU Delft - Geo-engineering)
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Abstract
In the Observational method Ab Initio approach observational feedback is used to optimize a structural design to field conditions that are found to be uncertain in the design phase. As a deep excavation takes place, the supporting retaining wall deflections can be observed by inclinometers which serve as an extra source of information on the structure’s performance. Although the Observational method can be beneficial for both safety and economic point of view, limited deep excavations have been executed via this design strategy. This is mainly due to the lack of specification on how measurement-processing can be used to support engineers to assess the structure’s safety. This study introduces a methodology for real-time measurement-processing with the use of Bayesian updating. In this methodology retaining wall deflections are used to update the unique field conditions of the construction site. This methodology is applied to a measurement set gathered at the construction of a deep excavation in Groningen, The Netherlands, to demonstrate its potential to supplement the Observational method.