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This paper presents a new coupled urban change and hazard consequence model that considers population growth, a changing built environment, natural hazard mitigation planning, and future acute hazards. Urban change is simulated as an agent‐based land market with six agent types and six land use types. Agents compete for parcels with successful bids leading to changes in both urban land use—affecting where agents are located—and structural properties of buildings—affecting the building's ability to resist damage to natural hazards. IN‐CORE, an open‐source community resilience model, is used to compute damages to the built environment. The coupled model operates under constraints imposed by planning policies defined at the start of a simulation. The model is applied to Seaside, Oregon, a coastal community in the North American Pacific Northwest subject to seismic‐tsunami hazards emanating from the Cascadia Subduction Zone. Ten planning scenarios are considered including caps on the number of vacation homes, relocating community assets, limiting new development, and mandatory seismic retrofits. By applying this coupled model to the testbed community, we show that: (a) placing a cap on the number of vacation homes results in more visitors in damaged buildings, (b) that mandatory seismic retrofits do not reduce the number of people in damaged buildings when considering population growth, (c) polices diverge beyond year 10 in the model, indicating that many policies take time to realize their implications, and (d) the most effective policies were those that incorporated elements of both urban planning and enforced building codes. Plain Language Summary: Natural hazards negatively impact communities resulting in significant infrastructure damages. Natural hazard mitigation planning attempts to reduce these damages and modeling can be used to measure how effective different mitigation plans can be. A new modeling framework is presented that accounts for population growth, a changing built environment, natural hazard mitigation planning, and future hazards. The model is applied to a testbed community with a large tourist population that is exposed to earthquake and tsunami hazards. Using this model, we consider different combinations of policies such as limiting the number of vacation homes in the community, relocating community assets, limiting new development, and enforcing building codes. Interestingly, we show that while placing a cap on the number of vacation homes does free up housing for full time residents, this also results in more visitors in damaged buildings. It is also shown how even with building codes in place, population growth contributes to an increased number of people in damaged buildings. Lastly, we show how the most effective policies incorporate elements of both urban planning and building codes. Key Points: An urban change model is coupled with a hazard consequence model to consider future hazards, population growth, and planning policiesThe model is applied to a coastal community in the Pacific Northwest considering Cascadia Subduction Zone seismic‐tsunami hazardsPlacing a cap on the number of vacation homes in a community could result in more visitors in damaged buildings [ABSTRACT FROM AUTHOR]

This paper presents a framework to evaluate the regional and local resilience of infrastructure networks following disruptions from natural hazards. Herein, the regional resilience of a network relates to the accessibility of a community within a larger network, whereas the local resilience concerns the ability of a network to provide its intended service within the boundaries of a community. Using this framework, a methodology is developed to demonstrate its application to a road and highway transportation network disrupted by ground shaking and inundation under a Cascadia Subduction Zone earthquake and tsunami scenario. The regional network extents encompass the entire coast of the US state of Oregon. Embedded within this regional network are 18 local networks associated with coastal communities. Regional and local connectivity indexes are defined to identify the initial damage and then track the postdisaster recovery of the transportation network, i.e., evaluate the network resilience. The study results identify the attributes that lead to a regionally or locally resilient network and highlight the importance of considering local infrastructure networks embedded within larger regional networks. It is shown that without regional considerations, the time to recover may be severely underpredicted. The methodology is further used as a decision support tool to demonstrate how mitigation options impact the transportation network's resilience. The importance of strategically considering mitigation options is emphasized as some communities see significant reductions in time to recover, whereas others see little to no improvement. [ABSTRACT FROM AUTHOR]

• Decision support framework for quantifying and improving infrastructure resilience. • Framework considers multiple hazards and interdependence between infrastructure. • Framework also includes decision support options and community expectations. • Resilience = P (robustenss ∩ rapidity) and is quantified using a Bayesian network. • Framework is applied to Seaside, Oregon, for seismic ground shaking and tsunami hazards. A probabilistic decision support framework is developed in this study for community resilience planning under multiple hazards using performance goals based guidelines such as the Oregon Resilience Plan and the National Institute of Standards and Technology Community Resilience Planning Guide. Herein, resilience of community infrastructure systems is defined as the joint probability of achieving robustness and rapidity based performance goals, which is quantified using Bayesian networks. The framework assesses the effects of decision support options such as selection of hazards, resilience goals, and mitigation (ex-ante) and response (ex-post) strategies to identify measures that can improve infrastructure performance to meet community defined resilience goals. This framework is applied for resilience assessment of building, transportation, water, and electric power infrastructure systems in Seaside, Oregon, under combined earthquake ground shaking and tsunami inundation hazards corresponding to different return periods. Uncertainties in damage, restoration, and economic losses are explicitly considered and propagated in the framework using Monte Carlo simulation (MCS). The MCS results are then used to inform the Bayesian network, which evaluates the joint resilience of infrastructure systems in Seaside. Results highlight the impact of considering different performance goals, introduction of ex-ante and ex-post measures, and interdependencies between various infrastructure systems on infrastructure resilience. [ABSTRACT FROM AUTHOR]