Implementation of Emerging Technologies in Seismic Risk Estimation

İhsan E. Bal
Hanze University of Applied Sciences, Groningen, Netherlands

Ever increasing population in seismically active urban areas, aging building stock, and expansion of urbanization to previously agricultural lands with soft soil deposits render the protection of human lives against earthquake disasters extremely more difficult by the time. Although much effort is put in further improving the current seismic design practices for new buildings, recent earthquakes show us, again and again, that life losses occur in older and much more vulnerable structures. Finding those substandard, collapse-vulnerable buildings before a destructive earthquake is like finding a needle in a haystack. It is clear that the problem in hand cannot be addressed with the existing, and mostly old-fashioned tools anymore. 

The most challenging task of the earthquake engineering community in the coming decade is spotting collapse vulnerable structures before they cause life losses in the next strong earthquake. This task requires a more in-depth analysis of the seismic risk, as well as decreasing the epistemic uncertainty bands in hazard, exposure and vulnerability components. One plausible way of addressing this issue is exploiting the emerging technologies in collection and processing of large amounts of data for constructing detailed exposure and vulnerability datasets. The resolution of the seismic risk estimation can then be set to individual buildings in this way. Existing AI, image processing, edge computing and satellite technologies offer a wide spectrum of applications in all disciplines of engineering, so it is a high time that such technologies were implemented for protecting lives against earthquakes. This talk focuses on example applications in treating the exposure and vulnerability components by taking advantage of the emerging technologies. The primary aim of the presented applications will be to show ways of increasing the resolution of seismic risk estimations to individual building level and thus substantially decreasing the associated uncertainties.