Multi-mechanism reliability updating of smart quay walls

Abstract

Traditional geotechnical design methodologies for quay walls rely on safety factors and characteristic soil properties to achieve safety; however, they can under-predict actual structural performance. Recent studies show that monitoring data can be used to update reliability calculations to thereby reduce
uncertainty, reveal hidden capacity, and extend the economic lifetime of such structures. However, applications to real-life cases have been limited and focus on single failure mechanisms only. Therefore, the primary objective of this thesis is to develop a computationally efficient framework that uses monitoring data to update the reliability of a quay wall across multiple failure modes. Specifically, yielding of the quay wall structure, yielding of the anchor rod, and loss of overall stability are considered.

In the methodology, the probability density functions of a number of stochastic variables are updated and based on their prior and posterior distributions, the prior and posterior reliabilities are determined. This methodology is executed by coupling a Bayesian updating strategy via subset simulation (aBUSSuS) with a metamodelling technique using active learning Kriging interpolation (ERRAGA). The method uses a “master-shadow” strategy to train separate Kriging models while saving preliminary results, thereby significantly reducing the required number of computationally expensive Plaxis 2D simulations. This framework is validated on a simplified theoretical demonstration case before being applied to a more complex case study at the Maasvlakte in the Port of Rotterdam. This latter case uses deformation data gathered by ShapeAccelArray (SAA) instruments during a full-scale load test.

The demonstration case proved the viability of the method: with targeted Plaxis runs, the probability of failure could be obtained for all three failure modes. Furthermore, the posterior deformations merged toward the fictitious measurements and the probability was updated accordingly. Saving results and
the recycling of Plaxis realizations more than halved the required number of finite element runs for this case. For the Maasvlakte - Port of Rotterdam case, the finite element model was more complex and hence additional strategies were implemented to guarantee convergence within 24 hours. Here, an a priori sensitivity analysis identified the parameters with the greatest impact on the model output, ensuring that computational resources were focused on the variables undergoing the most significant updates. By recycling finite element results across the different steps and optimising the Kriging convergence criteria, the method was then able to quantify and update the reliability of the three failure mechanisms within a practically viable 24-hour window. Ultimately, even with the integration of global model uncertainty, the framework resulted in significant reliability updates for the complex, full-scale quay wall.

The main conclusion of this research is therefore that multi-mechanism reliability updating using monitoring data is viable for full-scale quay walls and offers a way to reduce over-conservatism in existing designs. However, model inaccuracies heavily influence the outcomes and under the presented approach,
conservative models can lead to non-conservative updates by inducing disproportionately large parameter shifts. To fully replicate the field measurements for the Maasvlakte case, the soil friction angles had to be pushed beyond their realistic physical limits, which drove up posterior reliability levels.
The discrepancies are largely attributed to the limitations of the 2D plane-strain modelling. Hence, future research should focus on a fundamental revision of the underlying Plaxis model by transitioning to a 3D setup and a more precise derivation of expected parameter values. To cope with the increased computational demand of 3D simulations, acceleration techniques such as parallel computing should be integrated. Furthermore, future studies should account for additional failure modes and correlations between failure modes to derive a comprehensive system probability of failure.