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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]

Bridges exposed to flooding, hurricanes, tsunamis, and other extreme hydrologic events have been observed to fail due to deck dislodgement, pier failure, or foundation failure. However, the risk assessment and retrofit methodologies for these bridges have typically only been developed around a single failure mode. This paper addresses this gap by integrating the three observed failure modes for bridges vulnerable to extreme hydraulic events into a comprehensive risk assessment framework. Through the use of an event tree, the methodology accounts for the different consequences of failure associated with different failure modes. Bridge management strategies are investigated to determine the effectiveness of the retrofit actions with respect to their benefit (i.e. reduction in risk) and costs. An illustrative example for riverine bridges under various exposure scenarios is presented. The risk assessment and benefit-cost analysis elucidate the need to incorporate all pertinent failure modes of the structure by highlighting the competing nature of different failure modes. The illustrative example shows that the effective management of structures is site-specific and, that the intensity of the hazard at the bridge location affects which management strategy is preferred. The sensitivity to exposure level indicates that the optimal management of the structure should incorporate considerations for potential future changes in the intensity and frequency of the hazard. [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]

Fatigue is one of the main deteriorating mechanisms that affect the safety and reliability of ship structures. Fatigue cracks can appear at various locations along the ship structure and may occur at early stages in the service life of a ship. Inspection, monitoring and/or repair actions are applied to prevent sudden failures of damaged structural components and their associated consequences. However, these actions increase the operational cost of the ship and should be optimally planned during its service life. Due to the presence of significant uncertainties associated with crack initiation and propagation, the planning of such actions should be performed probabilistically. In this paper, a probabilistic approach for inspection, monitoring, and maintenance optimization for ship details under fatigue effects is proposed. Based on the stress profile and the crack geometry at the damaged location, intervention times and types are determined by solving an optimization problem which simultaneously minimizes the life-cycle cost, maximizes the expected service life, and minimizes the expected maintenance delay over the life-cycle. The life-cycle cost includes the cost of inspection, monitoring, and maintenance actions, as well as the cost of failure of the detail. The proposed approach is applied to a side shell detail of a steel ship. [ABSTRACT FROM AUTHOR]