Uncertainty Quantification for Naval Ships and the Optimal Adaptation of Bridges to Climate Change

Abstract Repairing and adapting existing structures and infrastructure is essential for maintaining the functionality of a transportation network and the flow of people, goods, and ideas across a region. However, structures are vulnerable to extreme events, such as hurricanes and floods, and continuous deterioration, due to exposure to corrosive environments and cyclic loading. The occurrence of extreme events may be nonstationary over the service life of the structures, leading to uncertain future loading conditions on the structure. Continuous deterioration, due to corrosion or fatigue, changes the capacity of the structure to resist loads over time. Repair and adaptation measures may be applied to a structure in order to improve the capacity to resist loads. However, limited economic resources prohibit the immediate repair and adaptation of all structures, thus requiring a systematic methodology be established prioritizing actions. It is because of this need that the field of life-cycle management has emerged. The focus of the research in this dissertation is on enhancing this field and the ability of engineers to (1) quantify uncertainty in the life-cycle management problem, (2) assess the performance of structures and develop effective management strategies, and (3) integrate the uncertainties of climate changes and future loading conditions into the management of structures.Uncertainty quantification typically involves describing the variability in the loads acting on a structure, the capacity of the structure, and the deterioration over time of the structure. In the design phase, uncertainty quantification is based on observing loads in the area (traffic, wind, hydraulic loads, etc) and testing materials and connections to characterize their properties. In the operational phase, Structural Health Monitoring (SHM) data can be integrated into the uncertainty quantification process. This research specifically enhances the ability to integrate SHM data into the fatigue life prediction of ship structures and improve uncertainty quantification for naval ships.Life-cycle management integrates the quantifiable uncertainties into the performance assessment of a structure. For civil structures, hydraulic hazards like hurricanes, floods, and tsunamis may cause extensive damage; and failure may have major economic, societal, and environmental consequences. This research focuses on enhancing the performance assessment methodologies for evaluating the risk associated with the failure of riverine and coastal bridges once the uncertainties are known. The considerations for the multiple failure modes, as well as the multiple hazards, included in this research are shown to be essential when determining the risk level of bridges. Furthermore, this work includes proposed methodologies for determining optimal management strategies that are driven by both performance and cost in order to aid decision makers.The final thrust area of this research emanates from the uncertainties associated with anticipated climate changes. Natural and anthropogenic changes result in changes to sea level, the intensity of storms, and the intensity of precipitation which leave riverine and coastal bridges increasingly vulnerable. The uncertainties that govern the future variability in climate are currently reported as unquantifiable. This type of uncertainty is referred to as a deep uncertainty and stems from the multiple feasible projections for gas concentrations and the multiple available climate models with which to evaluate them. This research introduces a systematic decision support framework for determining adaptation strategies in the presence of both the deep uncertainties of climate change and the quantifiable uncertainties of structural performance