A Novel Approach to Discriminate Between Structural and Non-Structural Post-Earthquake Damage in RC Structures

Abstract

In post-earthquake damage assessment studies of reinforced concrete (RC) buildings, the most important consideration is to determine the damage and severity of structural elements is the determination of the damage and severity of structural elements. For earthquake damage to structural elements, such as columns and shear walls, decisions can be made about strengthening or demolition. However, due to the need for rapid assessment of damaged buildings after earthquakes, these two damage mechanisms can sometimes be confused. Non-structural damage is classified as structural, leading to uneconomic structural decisions such as demolition or strengthening, or conversely, life safety issues arise from misidentifying critical structural damage with non-structural damage. In this study, an artificial intelligence-based damage assessment algorithm has been developed to accurately and quickly differentiate between structural and non-structural damage. For the damage classification model, a deep learning algorithm was developed using the 9680 damage images obtained from field studies after the recent earthquakes of Mw ≥ 5; Istanbul-Silivri (Mw: 5.8), Elazığ-Sivrice (Mw: 6.8) and Izmir-Seferihisar (Mw: 6.6) in Turkey. With an accuracy rate between 93% and 96%, the models constructed by selecting the optimal values correctly detected and categorised structural and non-structural element damages in RC structures. The method developed in this study can help experts in damage assessment studies as a decision support mechanism.