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According to a survey by the Ministry of the Interior (MOI) in Taiwan, around half of the 8.93 million buildings in the country, which are over 30 years old, have inadequate seismic capacity due to outdated design standards or aging materials. To evaluate seismic capacity, a preliminary seismic evaluation (PSE) system that involves site investigation and shop drawing review (if available) by professional engineers is typically used. However, given the significant financial and manpower resources required, performing PSE on all buildings in Taiwan is not practical. In order to overcome the challenge of evaluating the seismic capacity of buildings in a cost-effective and efficient manner, this study developed an enhanced PSE system called QSEBS, based on deep learning technology. By leveraging government property tax databases, QSEBS can rapidly estimate the seismic capacity of buildings, with results consistent with those of the PSERCB system. The key advantage of QSEBS is its ability to eliminate the need for human labors in PSE, saving significant amounts of money and manpower, particularly for a large number of buildings. Thus, QSEBS can serve as a valuable tool to support the government’s urban disaster-prevention strategy and can be widely implemented.

AI-based system for quick seismic estimation of building structures on urban disaster-prevention in Taiwan