| Current post-earthquake inspection of structures relies on certified inspectors to make an assessment of the existing safety of the structure based primarily on qualitative measures. Completing the required inspection can take weeks, which has adverse economic and societal impacts. This research contributes to the development of a semi-automated procedure for post-earthquake inspection and assessment of reinforced concrete (RC) buildings. Under the proposed framework, the visible damage inflicted on RC members is detected using machine vision. The damage properties are then measured in relation to the column's dimensions and orientation, so that the existing state of the column can be approximated as a damage index. The column damage index is then used to query fragility curves of similar buildings, constructed from the analyses of existing and on-going analytical data. The framework is expected to automate the collection of building damage data, to provide a quantitative assessment of the building damage state and to estimate the vulnerability of the building in the event of an aftershock.;Column failure is a primary cause of collapse for RC buildings; thus, assessing the structural integrity of RC columns after a seismic event is critical to evaluating the collapse risk of a damaged structure. Data from previous experimental tests of concrete columns is used to establish a link between the observed damage and structural performance state of the column and develop fragilities characterizing the collapse risk of a damaged structure subjected to an aftershock of variable intensity. These research results will be combined with the research addressing image processing and with other fragility functions to achieve the overall project objectives described above. |
