Advancing rapid visual screening method: An AI-integrated and automated data-driven approach for building vulnerability assessment

Abstract Buildings constructed prior to the implementation of seismic design standards or those built based on lower standards are susceptible to earthquake risks, resulting in substantial loss of life and property during an imminent earthquake. Although conventional rapid visual screening (RVS) methods have been extensively developed, both nationally and in the literature, they have limitations in accurately determining the vulnerability of buildings. Additionally, RVS methods developed on the basis of a single algorithm have limitations. Therefore, this study extends the existing body of work by integrating multiple AI algorithms, including fuzzy logic, machine learning, and neural networks, in the context of building damage data from the 2015 Gorkha earthquake, overcoming the limitations of previous studies by introducing an automated AI-based RVS methodology that enhances accuracy, transparency, and adaptability. The newly developed RVS method demonstrates an accuracy rate of 45.89% for testing in the three-class classification, while also delivering promising results in the two-class classification, with an accuracy rate of 60%, surpassing both conventional RVS methods and the baseline accuracy rate.