Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms

• Main Authors: Demertzis, K. (Author), Iliadis, L. (Author), Kavvadias, I.E (Author), Lazaridis, P.C (Author), Vasiliadis, L.K (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: artificial neural network; damage accumulation; intensity measures; machine learning; machine learning algorithms; repeated earthquakes; seismic sequence; structural damage prediction
• Online Access: View Fulltext in Publisher

 Advanced machine learning algorithms have the potential to be successfully applied to many areas of system modelling. In the present study, the capability of ten machine learning algorithms to predict the structural damage of an 8-storey reinforced concrete frame building subjected to single and successive ground motions is examined. From this point of view, the initial damage state of the structural system, as well as 16 well-known ground motion intensity measures, are adopted as the features of the machine-learning algorithms that aim to predict the structural damage after each seismic event. The structural analyses are performed considering both real and artificial ground motion sequences, while the structural damage is expressed in terms of two overall damage indices. The comparative study results in the most efficient damage index, as well as the most promising machine learning algorithm in predicting the structural response of a reinforced concrete building under single or multiple seismic events. Finally, the configured methodology is deployed in a user-friendly web application. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.