A Rapid Seismic Damage Assessment (RASDA) Tool for RC Buildings Based on an Artificial Intelligence Algorithm

In the current manuscript, a novel software application for rapid damage assessment of RC buildings subjected to earthquake excitation is presented based on artificial neural networks. The software integrates the use of a novel deep learning methodology for rapid damage assessment into modern software development platforms, while the developed graphical user interface promotes the ease of use even from non-experts. The aim is to foster actions both in the pre- and post-earthquake phase. The structure of the source code permits the usage of the application either autonomously as a software tool for rapid visual inspections of buildings prior to or after a strong seismic event or as a component of building information modelling systems in the framework of digitizing building data and properties. The methodology implemented for the estimation of the RC buildings' damage states is based on the theory and algorithms of pattern recognition problems. The effectiveness of the developed software is successfully tested using an extended, numerically generated database of RC buildings subjected to recorded seismic events.