Assimilate

This project will combine low-cost devices with machine learning technology to develop a platform for vulnerability and damage assessment. To this end, a large number of buildings will be monitored, and the recorded data will be used to develop a framework to calibrate numerical models relying on machine learning technology. Moreover, data recorded from past events will be explored to expand the framework to the estimation of damage and losses using monitoring data captured in near-real time. The consortium involves partners with decades of experience in seismic monitoring, vulnerability modelling and processing of large datasets using machine learning. Such an approach will mitigate several limitations in the current practice regarding vulnerability assessment and rapid loss estimation. This project has several stakeholders such as the Portuguese Civil Protection Authority, the Portuguese Institute of the Sea and Atmosphere (which monitors seismic activity), the National Laboratory of Civil Engineering, the Global Earthquake Model Foundation and SafeHub. It also contributes to the goals of the international agendas of the Sendai Framework and the United Nations 17 Sustainable Development Goals, which ask specifically to better understand risk and reduce the impact of natural hazards.

The project runs from to with a budget of 207.260 euros.

The following documents and deliverables are available: 


Members: