Predicting extreme events : the role of big data in quantifying risk in structural development

Abstract

Engineers are well-placed when calculating the required resistance for natural and non-natural hazards. However, there are two main problems with the current approach. First, while hazards are one of the primary causes of catastrophic damage and the design against risk contributes vastly to the cost in design and construction, it is only considered late in the development process. Second, current design approaches tend to provide guidelines that do not explain the rationale behind the presented values, leaving the engineer without any true understanding of the actual risk of a hazard occurring. Data is a key aspect in accurate prediction, though its sources are often sparsely distributed and engineers rarely have the background in statistics to process this into meaningful and useful results. This thesis explores the existing approaches to designing against hazards, focussing on natural hazards such as earthquakes, and the type of existing geographic information systems (GIS) that exist to assist in this process. A conceptual design for a hazard-related GIS is then proposed, looking at the key requirements for a system that could communicate key hazard-related data and how it could be designed and implemented. Sources for hazard-related data are then discussed. Finally, models and methodologies for interpreting hazard-related data are examined, with a schematic for how a hazard focussed system could be structured. These look at how risk can be predicted in a transparent way which ensures that the user of such a system is able to understand the hazard-related risks for a given location.