Abstract

One of the most rapidly emerging measures in infrastructure asset management is Structural Health Monitoring (SHM), which aims at reducing uncertainty in structural performance by using monitoring equipment. As earthen flood defence structures typically have large strength uncertainties, such techniques can be particularly promising. However, insight in the key characteristics for successful SHM for flood defences is lacking, which hampers the practical implementation. In this study, we explore the benefits of pore pressure monitoring, one of the most promising SHM techniques for earthen flood defences. The approach is based on a Bayesian pre-posterior analysis, and results are evaluated based on the Value of Information (VoI) obtained from different monitoring strategies. We specifically investigate the effect on long-term reinforcement decisions. The results show that, next to the relative magnitude of reducible uncertainty, the combination of the probability of having a useful observation and the duration of a SHM effort determine the VoI. As it is likely that increasing loads due to climate change will result in more frequent future reinforcements, the influence of scenarios of different rates of increase in future loads is also investigated. It was found that, in all considered possible scenarios, monitoring yields a positive Value of Information, hence it is an economically efficient measure for flood defence asset management both now and in the future.

Highlights

  • Over the past decades, the interest in infrastructure asset management has increased significantly [1]

  • We study the Value of Information (VoI) of different asset management strategies based on their performance over a time span of 200 years

  • We explore whether an investment, in efforts to reduce epistemic uncertainties, is robust in the sense that the VoI is positive for the entire range of possible rates of water level increase

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Summary

Introduction

The interest in infrastructure asset management has increased significantly [1]. An important development in the field of infrastructure asset management is the increasing popularity of Structural Health Monitoring (SHM). A Bayesian pre-posterior analysis is a popular method to determine this utility value, for which the general approach has been outlined in [10,11,12], and examples are given in [13,14] amongst others. This method is used in this paper

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