Speaker
Description
Exposure assessment is paramount for disaster risk reduction, but there are still many open challenges to develop high-resolution, up-to-date reliable exposure models. Existing research efforts are focused on developing global-scale high-resolution exposure layers on the building level and enriching them with local features collected using different methods. Depending on the hazard at stake, some building parameters gain more importance with respect to others. In the case of earthquakes, the main exposure data to be collected are related to material, age and height of the buildings, which allow classifying them under existing taxonomies. Some of the collected building characteristics (e.g. height) support preliminary estimation of their fundamental period, which is a ‘proxy’ of their dynamic response. These estimates are mostly based on empirical relationships derived for specific study areas using ambient noise measurements performed on selected buildings of given material and height. This information is rarely used for exposure assessment, but can be beneficial to perform new damage assessments methods that e.g. account for the dynamic behavior of buildings, modeling them as simple dynamic oscillators using their fundamental frequency. Here, we demonstrate how global-scale exposure layers can benefit from empirically-derived information from a local-scale building dataset. We leverage the existing knowledge collected for previously characterized study areas and integrate them into the global building-by-building model. We provide an example of application of damage assessment methods and discuss its relevance for disaster risk reduction purposes.
