RESUMEN
A better understanding of the uncertainty that exists in models used for seismic risk assessment is critical to improving risk-based decisions pertaining to earthquake safety. Current models estimating the probability of collapse of a building do not consider comprehensively the nature and impact of uncertainty. This article presents a model framework to enhance seismic risk assessment and thus gives decisionmakers a fuller understanding of the nature and limitations of the estimates. This can help ensure that risks are not over- or underestimated and the value of acquiring accurate data is appreciated fully. The methodology presented provides a novel treatment of uncertainties in input variables, their propagation through the model, and their effect on the results. The study presents ranges of possible annual collapse probabilities for different case studies on buildings in different parts of the world, exposed to different levels of seismicity, and with different vulnerabilities. A global sensitivity analysis was conducted to determine the significance of uncertain variables. Two key outcomes are (1) that the uncertainty in ground-motion conversion equations has the largest effect on the uncertainty in the calculation of annual collapse probability; and (2) the vulnerability of a building appears to have an effect on the range of annual collapse probabilities produced, i.e., the level of uncertainty in the estimate of annual collapse probability, with less vulnerable buildings having a smaller uncertainty.
RESUMEN
Understanding how container routing stands to be impacted by different scenarios of liner shipping network perturbations such as natural disasters or new major infrastructure developments is of key importance for decision-making in the liner shipping industry. The variety of actors and processes within modern supply chains and the complexity of their relationships have previously led to the development of simulation-based models, whose application has been largely compromised by their dependency on extensive and often confidential sets of data. This study proposes the application of optimisation techniques less dependent on complex data sets in order to develop a quantitative framework to assess the impacts of disruptive events on liner shipping networks. We provide a categorization of liner network perturbations, differentiating between systemic and external and formulate a container assignment model that minimises routing costs extending previous implementations to allow feasible solutions when routing capacity is reduced below transport demand. We develop a base case network for the Southeast Asia to Europe liner shipping trade and review of accidents related to port disruptions for two scenarios of seismic and political conflict hazards. Numerical results identify alternative routing paths and costs in the aftermath of port disruptions scenarios and suggest higher vulnerability of intra-regional connectivity.