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In order to adapt civil infrastructure to changing climate conditions, quantifiable and deep uncertainties must be integrated into the decision-making process. The quantifiable uncertainties, i.e. variability for which a likelihood can be defined, are typically integrated into the management process by considering the reliability or risk level of a structure. The deep uncertainties, i.e. the variability for which a likelihood cannot be defined, are beginning to be integrated in the decision making process as a few robust decision making procedures have been proposed. However, the deep uncertainty associated with the multiple feasible future climate scenarios also provokes a “wait and see” mentality for some decision makers, causing the flexibility of a strategy to be valued. This paper introduces the Gain-Loss Ratio (GLR) as a metric that systematically quantifies what may be gained by postponing adaptation while also considering what is lost with the delay. Additionally, bi-objective optimization models for optimizing bridge adaptation strategies under deep uncertainties are proposed; the advantages and disadvantages of each are highlighted as they pertain to the management of a typical riverine bridge. Two rivers are considered that have comparable climate change trends as those predicted for the Columbia and Mississippi Rivers. It is demonstrated that the desire for flexibility may be justified for certain locations, but may be detrimental in others. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) can be beneficial in reducing epistemic uncertainties associated with fatigue life prediction. For naval ships, available SHM data can be discretized into operational cells, each referring to a certain navigation speed, heading angle, and sea condition. Cell-based approaches for predicting future fatigue life can be applied if monitoring information is known for all cells. However, available SHM data may populate some, but not all, potential cells. Moreover, since SHM data is only available for a given set of operating conditions, potential changes in climate or operational profiles cannot be accounted for. Accordingly, there is a need for an approach to predict structural responses in unmonitored cells as a function of limited available monitoring data. This paper proposes a methodology to predict the responses of naval vessels in unobserved cells by incorporating data from the limited number of observed cells. The power spectral density (PSD) of the SHM data is fit using generalized functions, based on sea wave spectra, and integrated into the prediction of the PSD for unobserved cells. The proposed methodology enables both spectral and time-domain fatigue methods. The methodology is illustrated on the SHM data from a high speed aluminum catamaran. [ABSTRACT FROM AUTHOR]