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ABSTRACT
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half of the 8.93 million buildings in the country, which are over 30 years old, have inadequate seismic capacity due to outdated design standards or aging materials.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.
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Nomenclature
| Ac2 | = | seismic-capacity index |
| A2500 | = | seismic demand for a 2500-year return period earthquake |
| Ac2/IA2500 | = | seismic capacity-demand ratio for seismic vulnerability assessment |
| C | = | ratio of spectral acceleration divided by ground acceleration for a specific structural period in elastic normalized response spectrum of acceleration |
| D | = | diameter of the rebars and stirrups |
| E | = | convenient representation of |
| E_TACW | = | equivalent total area of column-wall |
| E_W/CW | = | equivalent width per column-wall |
| E_D/CW | = | equivalent depth per column-wall |
| H | = | value of Kruskal-Wallis H test |
| H0 | = | null hypotheses for correlation evaluation |
| I | = | importance factor |
| R | = | response reduction factor |
| Sa | = | parameter of elastic design spectral acceleration response |
| Tn | = | structural period |
| Vu, e | = | ultimate elastic base shear demand |
| Vy | = | yield base shear demand |
| VS30 | = | average shear wave velocity for a soil depth of 30 m |
| W | = | sum of weight lumped at the ground floor’s ceiling level |
| μ | = | ductility level |
| △u | = | ultimate or code-specified displacement |
| △y | = | yield displacement |
| χ2 | = | Chi-square value |
Disclosure statement
No potential conflict of interest was reported by the author(s).