Automated measurement of concrete spalling through reinforcement detection

In emergency scenarios, immediate reconnaissance efforts are necessary. These efforts often take months to complete in full. While underway, building occupants are unable to return to their homes/businesses, and thus, the impact on the society of the disaster-stricken region is increased. In order to mitigate the impact, researchers have focused on creating a more efficient means of assessing the condition of buildings in the post-disaster state. In this paper, a machine vision-based methodology for real-time post-earthquake safety assessment is presented. A novel method of retrieving spalled properties on reinforced concrete (RC) columns in RC frame buildings using image data is presented. In this method, the spalled region is detected using a local entropy-based approach. Following this, the depth properties are retrieved using contextual information pertaining to the amount and type of reinforcement which is exposed. The method is validated using a dataset of damaged RC column images.