Vision-based survey method for extraordinary loads on buildings

The statistical modeling of extraordinary loads on buildings has been stagnant for decades due to the laborious and error-prone nature of existing survey methods, such as questionnaires and verbal inquiries. This study proposes a new vision-based survey method for collecting extraordinary load data by automatically analyzing surveillance videos. For this purpose, a crowd head tracking framework is developed that integrates crowd head detection and reidentification models based on convolutional neural networks to obtain head trajectories of the crowd in the survey area. The crowd head trajectories are then analyzed to extract crowd quantity and velocities, which are the essential factors for extraordinary loads. For survey areas with frequent crowd movements during temporary events, the equivalent dynamic load factor can be further estimated using crowd velocity to consider dynamic effects. A crowd quantity investigation experiment and a crowd walking experiment are conducted to validate the proposed survey method. The experimental results prove that the proposed survey method is effective and accurate in collecting load data and reasonable in considering dynamic effects during extraordinary events. The proposed survey method is easy to deploy and has the potential to collect substantial and reliable extraordinary load data for determining design load on buildings.