Identify seismically vulnerable unreinforced masonry buildings using deep learning: Fid-Bau Portal

Identify seismically vulnerable unreinforced masonry buildings using deep learning

Khan, Salman / Lang, Anna / Salvaggio, Carl

Every year, 60,000 lives are lost worldwide from disasters.¹ Building collapse during earthquakes account for the majority of these deaths.² Unreinforced masonry (URM) buildings are particularly vulnerable during seismic events due to the brittle nature of the construction material. Many communities have undertaken costly and timely mitigation programs to locate and retrofit or replace them before disaster strikes. An automated approach for identifying seismically vulnerable buildings using street level imagery has been met with limited success to this point with no promising results presented in literature. We achieved the best overall accuracy reported to date, at 83.6%, in identifying unfinished URM, finished URM, and non-URM buildings. Moreover, an accuracy of 98.8% was achieved for identifying both suspected URMs (finished or unfinished URM). We perform extensive empirical analysis to establish synergistic parameters on our deep neural network, namely ResNeXt-101-FixRes. Lastly, we present a visualization for the layers in the network to ascertain and demonstrate how the deep neural network can distinguish between material and geometric features to predict the type of URM building.